The Prompt Report: A Systematic Survey of Prompt Engineering Techniques
提示工程报告：提示工程技术系统性综述
Abstract
Generative Artificial Intelligence (GenAI) systems are increasingly being deployed across diverse industries and research domains. Developers and end-users interact with these systems through the use of prompting and prompt engineering. Although prompt engineering is a widely adopted and extensively researched area, it suffers from conflicting terminology and a fragmented ontological understanding of what constitutes an effective prompt due to its relatively recent emergence. We establish a structured understanding of prompt engineering by assembling a taxonomy of prompting techniques and analyzing their applications. We present a detailed vocabulary of 33 vocabulary terms, a taxonomy of 58 LLM prompting techniques, and 40 techniques for other modalities. Additionally, we provide best practices and guidelines for prompt engineering, including advice for prompting engineering ChatGPT and other state-of-the-art (SOTA) LLMs. We further present a meta-analysis of the entire literature on natural language prefix-prompting. As a culmination of these efforts, this paper presents the most comprehensive survey on prompt engineering to date.
生成式人工智能系统正越来越多地被部署到不同的行业和研究领域。开发者和最终用户通过使用提示和提示工程与这些系统进行交互。尽管提示工程是一个被广泛采用且被深入研究的领域，但由于其相对较新，它面临着术语冲突以及对什么构成有效提示的本体论理解碎片化的问题。我们通过整理提示技术的分类法并分析其应用，建立了对提示工程的结构化理解。我们提出了一个包含33个词汇项的详细词汇表、一个包含58种LLM提示技术的分类法，以及40种针对其他模态的技术。此外，我们还提供了提示工程的最佳实践和指南，包括针对ChatGPT和其他最先进LLM的提示工程建议。我们进一步对整个自然语言前缀提示的文献进行了元分析。作为这些努力的成果，本文呈现了迄今为止关于提示工程最全面的综述。
Introduction
Transformer-based LLMs are widely deployed in consumer-facing, internal, and research settings. Typically, these models rely on the user providing an input "prompt" to which the model produces an output in response. Such prompts may be textual—"Write a poem about trees."—or take other forms: images, audio, videos, or a combination thereof. The ability to prompt models, particularly prompting with natural language, makes them easy to interact with and use flexibly across a wide range of use cases.
基于Transformer的LLM被广泛部署于面向消费者、内部和研究环境中。通常，这些模型依赖于用户提供的输入“提示”，模型据此产生输出响应。此类提示可以是文本形式的——“写一首关于树的诗。”——也可以是其他形式：图像、音频、视频或其组合。提示模型的能力，尤其是使用自然语言进行提示，使得模型易于交互并能灵活地应用于广泛的用例。
Knowing how to effectively structure, evaluate, and perform other tasks with prompts is essential to using these models. Empirically, better prompts lead to improved results across a wide range of tasks (Wei et al., 2022b; Liu et al., 2023b; Schuhoff, 2022). A large body of literature has grown around the use of prompting to improve results and the number of prompting techniques is rapidly increasing.
了解如何有效地构建、评估提示以及用提示执行其他任务，对于使用这些模型至关重要。经验表明，更好的提示能在广泛的任务中带来更好的结果。围绕使用提示来改善结果，大量文献涌现，提示技术的数量正在迅速增加。
However, as prompting is an emerging field, the use of prompts continues to be poorly understood, with only a fraction of existing terminologies and techniques being well-known among practitioners. We perform a large-scale review of prompting techniques to create a robust resource of terminology and techniques in the field. We expect this to be the first iteration of terminologies that will develop over time. We maintain an up-to-date list of terms and techniques at LearnPrompting.org.
然而，由于提示是一个新兴领域，其使用方式仍未被很好地理解，只有一小部分现有的术语和技术为从业者所熟知。我们对提示技术进行了大规模回顾，旨在为该领域创建一个健壮的术语和技术资源库。我们期望这将是术语的首次迭代，并将随着时间推移而发展。我们在LearnPrompting.org维护着一个最新的术语和技术列表。
Scope of Study We create a broad directory of prompting techniques, that can be quickly understood and easily implemented for rapid experimentation by developers and researchers. To this end, we limit our study to focus on prefix prompts rather than cloze prompts, because modern LLM transformer architectures widely employ prefix prompts and provide robust support for both developers and researchers. Additionally, we refined our focus to hard (discrete) prompts rather than soft (continuous) prompts and leave out papers that make use of techniques using gradient-based updates (i.e. fine-tuning). Hard prompts contain only tokens (vectors) that correspond to words in the model's vocabulary, while soft prompts may contain tokens that have no corresponding word in the vocabulary.
研究范围 我们创建了一个广泛的提示技术目录，这些技术可以被开发者和研究人员快速理解并轻松实现，以进行快速实验。为此，我们将研究范围限定在前缀提示而非完形填空提示，因为现代LLM Transformer架构广泛采用前缀提示，并为开发者和研究人员提供了强大的支持。此外，我们将重点放在硬（离散）提示而非软（连续）提示上，并排除了那些使用基于梯度更新技术（即微调）的论文。硬提示只包含对应模型词汇表中单词的词元（向量），而软提示可能包含词汇表中没有对应单词的词元。
Finally, we only study task-agnostic techniques. These decisions keep the work approachable to less technical readers and maintain a manageable scope.
最后，我们只研究与任务无关的技术。这些决策使得这项工作对技术背景较浅的读者来说更易理解，并保持范围可控。
Sections Overview We conducted a machine-assisted systematic review grounded in the PRISMA process (Section 2.1) to identify 58 different text-based prompting techniques, from which we create a taxonomy with a robust terminology of prompting terms (Section 1.2).
章节概览 我们基于PRISMA流程进行了一项机器辅助的系统性综述，识别出58种不同的基于文本的提示技术，并据此创建了一个具有健壮提示术语体系的分类法。
Our goal is to provide a roadmap for the community when considering which prompting techniques to use (Figure 1.1). While much literature on prompting focuses on English-only settings, we also discuss multilingual techniques (Section 3.1). Given the rapid growth in multimodal prompting, where prompts may include media such as images, we also expand our scope to multimodal techniques (Section 3.2). Many multilingual and multimodal prompting techniques are direct extensions of English text-only prompting techniques.
我们的目标是为社区在选择使用哪种提示技术时提供一个路线图（图1.1）。虽然许多关于提示的文献聚焦于纯英语环境，我们也将讨论多语言技术。鉴于多模态提示的快速增长，其中提示可能包含图像等媒介，我们还将范围扩展到多模态技术。许多多语言和多模态提示技术是纯英语文本提示技术的直接扩展。
As prompting techniques grow more complex, they have begun to incorporate external tools, such as Internet browsing and calculators. We use the term "agents" to describe these types of prompting techniques (Section 4.1).
随着提示技术变得越来越复杂，它们开始整合外部工具，如互联网浏览和计算器。我们使用术语“智能体”来描述这些类型的提示技术。
It is important to understand how to evaluate the outputs of agents and prompting techniques to ensure accuracy and avoid hallucinations. Thus, we discuss ways of evaluating these outputs (Section 4.2). We also discuss security (Section 5.1) and safety measures (Section 5.2) for designing prompts that reduce the risk of harm to companies and users.
理解如何评估智能体和提示技术的输出以确保准确性并避免幻觉至关重要。因此，我们讨论了评估这些输出的方法。我们还讨论了设计提示的安全措施，以降低对公司及用户造成损害的风险。
Finally, we apply prompting techniques in two case studies (Section 6.1). In the first, we test a range of prompting techniques against the commonly used benchmark MMLU. In the second, we explore in detail an example of manual prompt engineering on a significant, real-world use case, identifying signals of frantic hopelessness—a top indicator of suicidal crisis—in the text of individuals seeking support (Schuck et al., 2019a). We conclude with a discussion of the nature of prompting and its recent development (Section 8).
最后，我们在两个案例研究中应用了提示技术。在第一个案例中，我们针对常用基准MMLU测试了一系列提示技术。在第二个案例中，我们详细探讨了一个重要的、真实世界用例上的人工提示工程实例，即在寻求支持的个体的文本中识别“疯狂绝望”的信号——这是自杀危机的首要指标。最后，我们讨论了提示的本质及其近期发展。
1.1 What is a Prompt?
A prompt is an input to a Generative AI model, that is used to guide its output. Prompts may consist of text, image, sound, or other media. Some examples of prompts include the text, "write a three paragraph email for a marketing campaign for an accounting firm", a photograph of a piece of paper with the words "what is 10*179" written on it, or a recording of an online meeting, with the instructions "summarize this". Prompts usually have some text component, but this may change as non-text modalities become more common.
1.1 什么是提示？
提示是生成式人工智能模型的输入，用于引导其输出。提示可以由文本、图像、声音或其他媒介组成。提示的一些示例包括文本“为一家会计公司的营销活动写一封三段的电子邮件”、一张纸上写着“10*179等于多少”的照片，或者一段在线会议的录音并附带指令“总结此内容”。提示通常包含一些文本成分，但随着非文本模态变得越来越普遍，这种情况可能会改变。
Prompt Template Prompts are often constructed via a prompt template. A prompt template is a function that contains one or more variables which will be replaced by some media (usually text) to create a prompt. This prompt can then be considered to be an instance of the template.
提示模板 提示通常通过提示模板构建。提示模板是一个包含一个或多个变量的函数，这些变量将被某些媒介（通常是文本）替换以创建提示。然后，这个提示可以被视为该模板的一个实例。
Consider applying prompting to the task of binary classification of tweets. Here is an initial prompt template that can be used to classify inputs.
考虑将提示应用于推文二分类任务。以下是一个可用于分类输入的初始提示模板。
Classify the tweet as positive or negative:
{TWEET}
将推文分类为正面或负面：
{推文}
Each tweet in the dataset would be inserted into a separate instance of the template and the resulting prompt would be given to a LLM for inference.
数据集中的每条推文将被插入到模板的一个单独实例中，生成的提示将提供给LLM进行推理。
Directive Many prompts issue a directive in the form of an instruction or question. This is the core intent of the prompt, sometimes simply called the "intent". For example, here is an instance of a prompt with a single instruction:
指令 许多提示以指令或问题的形式发出指令。这是提示的核心意图，有时简称为“意图”。例如，下面是一个带有单一指令的提示实例：
Tell me five good books to read.
告诉我五本值得读的好书。
Directives can also be implicit, as in this one-shot case, where the directive is to perform English to Spanish translation:
指令也可以是隐式的，如下面这个单样本（One-Shot）示例，其指令是执行英译西翻译：
Night: Noche
Morning:
Night: Noche
Morning:
Examples Examples, also known as exemplars or shots, act as demonstrations that guide the GenAI to accomplish a task. The above prompt is a One-Shot (i.e. one example) prompt.
示例 示例，也称为范例或样本，作为指导GenAI完成任务的演示。上面的提示就是一个单样本（即一个示例）提示。
Output Formatting It is often desirable for the GenAI to output information in certain formats, for example, CSV, Markdown, XML, or even custom formats. Structuring outputs may reduce performance on some tasks. However, Kurt (2024) point out various flaws in Tam et al. (2024) and show that structuring outputs may actually improve performance. Here is an example of how you might format a prompt to output information as a CSV:
输出格式化 通常希望GenAI以特定格式输出信息，例如CSV、Markdown、XML甚至自定义格式。结构化输出可能会降低某些任务的性能。然而，Kurt 指出了 Tam 等人研究中的各种缺陷，并表明结构化输出实际上可能提升性能。下面是一个如何格式化提示以输出CSV信息的示例：
{PARAGRAPH}
Summarize this into a CSV.
{段落}
将此内容总结为CSV格式。
Style Instructions Style instructions are a type of output formatting used to modify the output stylistically rather than structurally (Section 2.2.1.3). For example:
风格指令 风格指令是一种输出格式化类型，用于在风格上而非结构上修改输出（第 2.2.1.3 节）。例如：
Write a clear and curt paragraph about llamas.
写一段关于羊驼的清晰而简洁的段落。
Role A Role, also known as a persona, is a frequently discussed component that can improve writing and style text (Section 2.2.1.3). For example:
角色 角色，也称为人设，是一个经常被讨论的组件，可以改善文本的写作和风格（第 2.2.1.3 节）。例如：
Pretend you are a shepherd and write a limerick about llamas.
假设你是一个牧羊人，写一首关于羊驼的打油诗。
Additional Information It is often necessary to include additional information in the prompt. For example, if the directive is to write an email, you might include information such as your name and position so the GenAI can properly sign the email. Additional Information is sometimes called 'context', though we discourage the use of this term as it is overloaded with other meanings in the prompting space.
额外信息 在提示中常常需要包含额外信息。例如，如果指令是写一封电子邮件，你可能需要包含你的姓名和职位等信息，以便 GenAI 能正确地签署邮件。额外信息有时被称为“上下文”，尽管我们不鼓励使用这个术语，因为它在提示领域有太多其他含义。
1.2.2 Prompting Terms
Terminology within the prompting literature is rapidly developing. As it stands, there are many poorly understood definitions (e.g. prompt, prompt engineering) and conflicting ones (e.g. role prompt vs persona prompt). The lack of a consistent vocabulary hampers the community's ability to clearly describe the various prompting techniques in use. We provide a robust vocabulary of terms used in the prompting community (Figure 1.3). Less frequent terms are left to Appendix A.2. In order to accurately define frequently-used terms like prompt and prompt engineering, we integrate many definitions (Appendix A.1) to derive representative definitions.
1.2.2 提示术语
提示文献中的术语正在快速发展。目前，存在许多理解模糊的定义（例如，prompt、prompt engineering）以及相互冲突的定义（例如，role prompt 与 persona prompt）。缺乏一致的词汇阻碍了社区清晰描述各种使用中的提示技术的能力。我们提供了一个提示社区中使用的健壮词汇表（图 1.3）。较少使用的术语留待附录 A.2 处理。为了准确定义诸如 prompt 和 prompt engineering 等常用术语，我们整合了多种定义（附录 A.1）以得出具有代表性的定义。
Prompting Prompting is the process of providing a prompt to a GenAI, which then generates a response. For example, the action of sending a chunk of text or uploading an image constitutes prompting.
提示 提示是向生成式人工智能提供提示，然后由模型生成响应的过程。例如，发送一段文本或上传一张图片的行为即构成提示。
Prompt Chain A prompt chain (activity: prompt chaining) consists of two or more prompt templates used in succession. The output of the prompt generated by the first prompt template is used to parameterize the second template, continuing until all templates are exhausted.
提示链 提示链（活动：提示链式调用）由两个或多个依次使用的提示模板组成。由第一个提示模板生成的提示的输出用于参数化第二个模板，如此继续，直到所有模板用完。
Prompting Technique A prompting technique is a blueprint that describes how to structure a prompt, prompts, or dynamic sequencing of multiple prompts. A prompting technique may incorporate conditional or branching logic, parallelism, or other architectural considerations spanning multiple prompts.
提示技术 提示技术是一种蓝图，描述了如何构造一个提示、多个提示或多个提示的动态排序。提示技术可能包含条件或分支逻辑、并行性，或跨越多个提示的其他架构考量。
Prompt Engineering Prompt engineering is the iterative process of developing a prompt by modifying or changing the prompting technique that you are using (Figure 1.4).
提示工程 提示工程是通过修改或更改所使用的提示技术来迭代开发提示的过程（图 1.4）。
Prompt Engineering Technique A prompt engineering technique is a strategy for iterating on a prompt to improve it. In literature, this will often be automated techniques (Deng et al., 2022), but in consumer settings, users often perform prompt engineering manually, without any assistive tooling.
提示工程技术 提示工程技术是一种迭代改进提示以使其更优的策略。在文献中，这通常是自动化技术，但在消费级环境中，用户通常在没有辅助工具的情况下手动进行提示工程。
Exemplar Exemplars are examples of a task being completed that are shown to a model in a prompt (Brown et al., 2020).
示例 示例是在提示中向模型展示的已完成任务的样例。
1.3 A Short History of Prompts
The idea of using natural language prefixes, or prompts, to elicit language model behaviors and responses originated before the GPT-3 and ChatGPT era. GPT-2 makes use of prompts and they appear to be first used in the context of Generative AI by Fan et al. (2018). However, the concept of prompts was preceded by related concepts such as control codes and writing prompts in literature.
1.3 提示简史
使用自然语言前缀（即提示）来引发语言模型行为和响应的想法起源于 GPT-3 和 ChatGPT 时代之前。GPT-2 使用了提示，而 Fan 等人似乎是首次在生成式人工智能的背景下使用提示。然而，提示的概念之前已有相关概念，例如控制码和文学创作中的写作提示。
The term Prompt Engineering appears to have come into existence more recently from Radford et al. (2021) then slightly later from Reynolds and McDonell (2021).
“提示工程”这一术语似乎更晚些时候才出现，源自 Radford 等人，稍后又见于 Reynolds 和 McDonell 的著作。
However, various papers perform prompt engineering without naming the term (Wallace et al., 2019; Shin et al., 2020a), including Schick and Schütze (2020a,b); Gao et al. (2021) for non-autoregressive language models.
然而，许多论文在没有命名该术语的情况下进行了提示工程，包括针对非自回归语言模型的研究。
Some of the first works on prompting define a prompt slightly differently to how it is currently used. For example, consider the following prompt from Brown et al. (2020):
一些早期的提示研究工作对提示的定义与当前的使用略有不同。例如，考虑 Brown 等人给出的以下提示：
Translate English to French:
llama
将英语翻译成法语:
llama
Brown et al. (2020) consider the word "llama" to be the prompt, while "Translate English to French:" is the "task description". More recent papers, including this one, refer to the entire string passed to the LLM as the prompt.
Brown 等人将单词 "llama" 视为提示，而 "Translate English to French:" 是“任务描述”。最近的论文，包括本文，则将传递给 LLM 的整个字符串称为提示。
A Meta-Analysis of Prompting
2.1 Systematic Review Process
In order to robustly collect a dataset of sources for this paper, we ran a systematic literature review grounded in the PRISMA process (Figure 2.1). We host this dataset on HuggingFace and present a datasheet for the dataset in Appendix A.3. Our main data sources were arXiv, Semantic Scholar, and ACL. We query these databases with a list of 44 keywords narrowly related to prompting and prompt engineering (Appendix A.4).
2.1 系统综述流程
为了稳健地收集本文的文献来源数据集，我们基于PRISMA流程进行了一项系统文献综述。我们将此数据集托管在HuggingFace上，并在附录A.3中提供了该数据集的数据表。我们的主要数据来源是arXiv、Semantic Scholar和ACL。我们使用一份包含44个与提示和提示工程密切相关的关键词列表查询这些数据库（附录A.4）。
2.1.1 The Pipeline
In this section, we introduce our data scraping pipeline, which includes both human and LLM-assisted review. As an initial sample to establish filtering criteria, we retrieve papers from arXiv based on a simple set of keywords and boolean rules (A.4). Then, human annotators label a sample of 1,661 articles from the arXiv set for the following criteria:
- Include if the paper proposes a novel prompting technique.
- Include if the paper strictly covers hard prefix prompts.
- Exclude if the paper focuses on training by backpropagating gradients.
- Include if the paper uses a masked frame and/or window for non-text modalities.
2.1.1 流程管道
在本节中，我们介绍我们的数据抓取管道，其中包括人工和LLM辅助的评审。作为建立筛选标准的初始样本，我们基于一组简单的关键词和布尔规则从arXiv检索论文（附录A.4）。然后，人工标注员对arXiv集合中的1,661篇文章样本按照以下标准进行标注：
- 如果论文提出了一种新颖的提示技术，则纳入。
- 如果论文严格涵盖硬前缀提示，则纳入。
- 如果论文侧重于通过反向传播梯度进行训练，则排除。
- 如果论文针对非文本模态使用了掩码帧和/或窗口，则纳入。
A set of 300 articles are reviewed independently by two annotators, with 92% agreement (Krippendorff's
其中300篇文章由两位标注员独立评审，一致率为92%（Krippendorff's
2.2 Text-Based Techniques
We now present a comprehensive taxonomical ontology of 58 text-based prompting techniques, broken into 6 major categories (Figure 2.2). Although some of the techniques might fit into multiple categories, we place them in a single category of most relevance.
2.2 基于文本的技术
我们现在提出一个包含58种基于文本的提示技术的综合性分类学本体，这些技术被划分为6个主要类别（图2.2）。尽管有些技术可能适合多个类别，我们将它们归入最相关的一个类别中。
2.2.1 In-Context Learning (ICL)
ICL refers to the ability of GenAIs to learn skills and tasks by providing them with exemplars and or relevant instructions within the prompt, without the need for weight updates/retraining. These skills can be learned from exemplars (Figure 2.4) and/or instructions (Figure 2.5). Note that the word "learn" is misleading. ICL can simply be task specification—the skills are not necessarily new, and can have already been included in the training data (Figure 2.6). See Appendix A.9 for a discussion of the use of this term. Significant work is currently being done on optimizing and understanding ICL.
2.2.1 上下文学习
上下文学习指的是生成式人工智能通过在提示中提供示例和/或相关指令来学习技能和任务的能力，无需权重更新或重新训练。这些技能可以从示例（图2.4）和/或指令（图2.5）中学习。注意，“学习”一词可能具有误导性。上下文学习可以仅仅是任务说明——这些技能不一定是新的，可能已经包含在训练数据中（图2.6）。关于该术语使用的讨论见附录A.9。目前，在优化和理解上下文学习方面正在进行重要工作。
2+2: four
4+5: nine
8+0:
Figure 2.4: ICL exemplar prompt
2+2: four
4+5: nine
8+0:
图 2.4: 上下文学习示例提示
Extract all words that have 3 of the same letter and at least 3 other letters from the following text: {TEXT}
Figure 2.4: ICL instruction prompt
从以下文本中提取所有包含 3 个相同字母且至少还有 3 个其他字母的单词：{TEXT}
图 2.5：上下文学习指令提示示例
Translate the word "cheese" to French.
Figure 2.6: ICL from training data prompt. In this version of ICL, the model is not learning a new skill, but rather using knowledge likely in its training set.
将单词“cheese”翻译成法语。
图 2.6：来自训练数据提示的上下文学习。在此版本的 ICL 中，模型并非在学习新技能，而是在使用可能已存在于其训练集中的知识。
Few-Shot Prompting is the paradigm seen in Figure 2.4, where the GenAI learns to complete a task with only a few examples (exemplars). Few-shot prompting is a special case of Few-Shot Learning (FSL), but does not require updating of model parameters
少样本提示 是图2.4中所见的范式，其中生成式人工智能仅通过少量示例来学习完成任务。少样本提示是少样本学习的一种特殊情况，但它不需要更新模型参数。
2.2.1.1 Few-Shot Prompting Design Decisions
Selecting exemplars for a prompt is a difficult task—performance depends significantly on various factors of the exemplars, and only a limited number of exemplars fit in the typical LLM's context window. We highlight six separate design decisions, including the selection and order of exemplars that critically influence the output quality (Figure 2.3).
2.2.1.1 少样本提示设计决策
为提示选择示例是一项困难的任务——性能在很大程度上取决于示例的各种因素，且只有有限数量的示例能够放入典型LLM的上下文窗口中。我们重点介绍六个独立的设计决策，包括示例的选择和顺序，它们对输出质量有决定性影响（图2.3）。
Exemplar Quantity Increasing the quantity of exemplars in the prompt generally improves model performance, particularly in larger models. However, in some cases, the benefits may diminish beyond 20 exemplars. In the case of long context LLMs, additional exemplars continue to increase performance, though efficiency varies depending on task and model.
示例数量 增加提示中的示例数量通常会提升模型性能，尤其是在较大的模型中。然而，在某些情况下，超过20个示例后收益可能会递减。对于长上下文LLM，添加更多示例会持续提升性能，尽管效率因任务和模型而异。
Exemplar Ordering The order of exemplars affects model behavior. On some tasks, exemplar order can cause accuracy to vary from sub-50% to 90%+.
示例顺序 示例的顺序会影响模型行为。在某些任务上，示例顺序可能导致准确率从低于50%变化到90%以上。
Exemplar Label Distribution As in traditional supervised machine learning, the distribution of exemplar labels in the prompt affects behavior. For example, if 10 exemplars from one class and 2 exemplars of another class are included, this may cause the model to be biased toward the first class.
示例标签分布 与传统监督机器学习一样，提示中示例标签的分布会影响行为。例如，如果包含来自某一类别的10个示例和另一类别的2个示例，可能会导致模型偏向第一类。
Exemplar Label Quality Despite the general benefit of multiple exemplars, the necessity of strictly valid demonstrations is unclear. Some work suggests that the accuracy of labels is irrelevant—providing models with exemplars with incorrect labels may not negatively diminish performance. However, under certain settings, there is a significant impact on performance. Larger models are often better at handling incorrect or unrelated labels.
示例标签质量 尽管多个示例通常有益，但严格有效演示的必要性尚不明确。一些研究表明标签的准确性无关紧要——向模型提供带有错误标签的示例可能不会降低性能。然而，在某些设置下，对性能有显著影响。较大的模型通常更能处理错误或不相关的标签。
It is important to discuss this factor, since if you are automatically constructing prompts from large datasets that may contain inaccuracies, it may be necessary to study how label quality affects your results.
讨论这一因素很重要，因为如果你从可能包含不准确信息的大型数据集中自动构建提示，可能需要研究标签质量如何影响你的结果。
Exemplar Format The formatting of exemplars also affects performance. One of the most common formats is "Q: {input}, A: {label}", but the optimal format may vary across tasks; it may be worth trying multiple formats to see which performs best. There is some evidence to suggest that formats that occur commonly in the training data will lead to better performance.
示例格式 示例的格式也会影响性能。最常见的格式之一是“Q: {input}, A: {label}”，但最佳格式可能因任务而异；可以尝试多种格式以找出最佳效果。有证据表明，训练数据中常见的格式会带来更好的性能。
Exemplar Similarity Selecting exemplars that are similar to the test sample is generally beneficial for performance. However, in some cases, selecting more diverse exemplars can improve performance.
示例相似性 选择与测试样本相似的示例通常有利于性能提升。然而，在某些情况下，选择更多样化的示例可以改善性能。
Instruction Selection While instructions are required to guide LLMs in zero-shot prompts, the benefits of adding instructions before exemplars in few-shot prompts is less clear. Ajith et al. show that generic, task-agnostic instructions (i.e., no instruction or "Complete the following task:") improve classification and question answering accuracy over task-specific ones (e.g., What is the answer to this question?) concluding instruction-following abilities can be achieved via exemplars alone. While they may not improve correctness, instructions in few-shot prompts can still guide auxiliary output attributes like writing style.
指令选择 虽然指令对于在零样本提示中引导LLM是必需的，但在少样本提示中，在示例之前添加指令的好处尚不明确。Ajith等人表明，通用的、与任务无关的指令（即无指令或“完成以下任务：”）在分类和问答准确性上优于特定任务的指令（例如，“这个问题的答案是什么？”），结论是仅通过示例即可获得指令遵循能力。虽然它们可能不会提高正确性，但少样本提示中的指令仍然可以指导辅助输出属性，如写作风格。
2.2.1.2 Few-Shot Prompting Techniques
Considering all of these factors, Few-Shot Prompting can be very difficult to implement effectively. We now examine techniques for Few-Shot Prompting in the supervised setting. Ensembling approaches can also benefit Few-Shot Prompting, but we discuss them separately (Section 2.2.4).
2.2.1.2 少样本提示技术
考虑到所有这些因素，少样本提示可能很难有效实施。我们现在研究监督设置下的少样本提示技术。集成方法也有利于少样本提示，但我们将单独讨论它们（第2.2.4节）。
Assume we have a training dataset,
假设我们有一个训练数据集
K-Nearest Neighbor (KNN) is part of a family of algorithms that selects exemplars similar to
K近邻（KNN） 是一种选择与测试样本
Vote-K is another method to select similar exemplars to the test sample. In one stage, a model proposes useful unlabeled candidate exemplars for an annotator to label. In the second stage, the labeled pool is used for Few-Shot Prompting. Vote-K also ensures that newly added exemplars are sufficiently different than existing ones to increase diversity and representativeness.
Vote-K 是另一种选择与测试样本相似示例的方法。在第一阶段，模型提出有用的未标记候选示例供标注者标注。在第二阶段，标记好的示例池用于少样本提示。Vote-K还确保新添加的示例与现有示例有足够差异，以增加多样性和代表性。
Self-Generated In-Context Learning (SG-ICL) leverages a GenAI to automatically generate exemplars. While better than zero-shot scenarios when training data is unavailable, the generated samples are not as effective as actual data.
自生成上下文学习（SG-ICL） 利用生成式AI自动生成示例。虽然在训练数据不可用时优于零样本场景，但生成的样本不如实际数据有效。
Prompt Mining is the process of discovering optimal "middle words" in prompts through large corpus analysis. These middle words are effectively prompt templates. For example, instead of using the common "Q: A:" format for few-shot prompts, there may exist something similar that occurs more frequently in the corpus. Formats which occur more often in the corpus will likely lead to improved prompt performance.
提示挖掘 是通过大规模语料库分析发现提示中最佳“中间词”的过程。这些中间词实际上是提示模板。例如，与其为少样本提示使用常见的“Q: A:”格式，可能存在于语料库中出现频率更高的类似格式。在语料库中更常出现的格式可能会带来更好的提示性能。
More Complicated Techniques such as LENS, UDR, and Active Example Selection leverage iterative filtering, embedding and retrieval, and reinforcement learning, respectively.
更复杂的技术，如LENS、UDR和主动示例选择，分别利用迭代过滤、嵌入与检索以及强化学习。
{Exemplars}
Figure 2.7: Few-Shot Prompting Template
{Exemplars}
图 2.7：少样本提示模板
2.2.1.3 Zero-Shot Prompting Techniques
In contrast to Few-Shot Prompting, Zero-Shot Prompting uses zero exemplars. There are a number of well-known standalone zero-shot techniques as well as zero-shot techniques combined with another concept (e.g. Chain of Thought), which we discuss later (Section 2.2.2).
2.2.1.3 零样本提示技术
与少样本提示不同，零样本提示不使用任何示例。有许多众所周知的独立零样本技术，以及与其他概念（例如思维链）相结合的零样本技术，我们将在后面讨论（第2.2.2节）。
Role Prompting , also known as persona prompting, assigns a specific role to the GenAI in the prompt. For example, the user might prompt it to act like "Madonna" or a "travel writer". This can create more desirable outputs for open-ended tasks and in some cases may improve accuracy on benchmarks.
角色提示 也称为人设提示，是在提示中为生成式AI分配特定角色。例如，用户可以提示它扮演"麦当娜"或"旅行作家"。这可以为开放式任务创建更理想的输出，在某些情况下可能提高基准测试的准确率。
Style Prompting involves specifying the desired style, tone, or genre in the prompt to shape the output of a GenAI. A similar effect can be achieved using role prompting.
风格提示 涉及在提示中指定期望的风格、语气或体裁，以塑造生成式AI的输出。使用角色提示可以达到类似效果。
Emotion Prompting incorporates phrases of psychological relevance to humans (e.g., "This is important to my career") into the prompt, which may lead to improved LLM performance on benchmarks and open-ended text generation.
情感提示 将与人类心理相关的短语（例如，"这对我的职业生涯很重要"）纳入提示中，这可能会提高LLM在基准测试和开放式文本生成中的表现。
System 2 Attention (S2A) first asks an LLM to rewrite the prompt and remove any information unrelated to the question therein. Then, it passes this new prompt into an LLM to retrieve a final response.
系统2注意力（S2A） 首先要求LLM重写提示，并删除其中与问题无关的任何信息。然后，将此新提示传递给另一个LLM以获取最终响应。
SimToM deals with complicated questions which involve multiple people or objects. Given the question, it attempts to establish the set of facts one person knows, then answer the question based only on those facts. This is a two prompt process and can help eliminate the effect of irrelevant information in the prompt.
SimToM 处理涉及多个人或多个物体的复杂问题。给定问题后，它试图建立一个人所知道的事实集，然后仅基于这些事实来回答问题。这是一个双提示过程，有助于消除提示中无关信息的影响。
Rephrase and Respond (RaR) instructs the LLM to rephrase and expand the question before generating the final answer. For example, it might add the following phrase to the question: "Rephrase and expand the question, and respond". This could all be done in a single pass or the new question could be passed to the LLM separately. RaR has demonstrated improvements on multiple benchmarks.
重述与回答（RaR） 指示LLM在生成最终答案之前重述并扩展问题。例如，它可以在问题中添加以下短语："重述并扩展问题，然后回答"。这可以在单次传递中完成，也可以将新问题单独传递给LLM。RaR已在多个基准测试中显示出改进。
Re-reading (RE2) adds the phrase "Read the question again:" to the prompt in addition to repeating the question. Although this is such a simple technique, it has shown improvement in reasoning benchmarks, especially with complex questions.
重读（RE2） 除了重复问题外，还在提示中添加短语"再次阅读问题："。尽管这是一种简单的技术，但它已在推理基准测试中显示出改进，尤其是在复杂问题上。
Self-Ask prompts LLMs to first decide if they need to ask follow up questions for a given prompt. If so, the LLM generates these questions, then answers them and finally answers the original question.
自我提问（Self-Ask） 提示LLM首先判断对于给定的提示是否需要提出后续问题。如果需要，LLM生成这些问题，然后回答它们，最后回答原始问题。
2.2.2 Thought Generation
Thought generation encompasses a range of techniques that prompt the LLM to articulate its reasoning while solving a problem.
2.2.2 思维生成
思维生成包含一系列技术，这些技术促使LLM在解决问题的同时阐述其推理过程。
Chain-of-Thought (CoT) Prompting leverages few-shot prompting to encourage the LLM to express its thought process before delivering its final answer.This technique is occasionally referred to as Chain-of-Thoughts. It has been demonstrated to significantly enhance the LLM's performance in mathematics and reasoning tasks. In Wei et al., the prompt includes an exemplar featuring a question, a reasoning path, and the correct answer (Figure 2.8).
思维链提示 利用少样本提示鼓励LLM在给出最终答案之前表达其思考过程。该技术有时也被称为"思维链"。它已被证明能显著提升LLM在数学和推理任务中的表现。在Wei等人的研究中，提示包含一个示例，其中包括一个问题、一条推理路径和正确答案（图2.8）。
Q: Jack has two baskets, each containing three balls. How many balls does Jack have in total?
A: One basket contains 3 balls, so two baskets contain
Q: {QUESTION}
A:
Figure 2.8: A One-Shot Chain-of-Thought Prompt
Q：杰克有两个篮子，每个篮子里有三个球。杰克总共有多少个球？
A：一个篮子有3个球，所以两个篮子有
Q：{问题}
A：
图 2.8：一个单样本思维链提示
2.2.2.1 Zero-Shot-CoT
The most straightforward version of CoT contains zero exemplars. It involves appending a thought inducing phrase like "Let's think step by step." to the prompt. Other suggested thought-generating phrases include "First, let's think about this logically". Zhou et al. uses LLMs to generate "Let's work this out in a step by step way to be sure we have the right answer". Yang et al. searches for an optimal thought inducer. Zero-Shot-CoT approaches are attractive as they don't require exemplars and are generally task agnostic.
2.2.2.1 零样本思维链
最直接的CoT版本不含任何示例。它涉及在提示后附加一个思维诱导词，例如"让我们一步步思考"。其他建议的思维生成短语包括"首先，让我们逻辑地思考这个问题"。Zhou等人使用LLM生成"让我们一步步解决这个问题，以确保我们得到正确答案"。Yang等人则搜索最优的思维诱导词。零样本CoT方法颇具吸引力，因为它们不需要示例，且通常与任务无关。
Step-Back Prompting is a modification of CoT where the LLM is first asked a generic, high-level question about relevant concepts or facts before delving into reasoning. This approach has improved performance significantly on multiple reasoning benchmarks for both PaLM-2L and GPT-4.
退一步提示 是CoT的一种改进，它首先向LLM提出一个关于相关概念或事实的通用、高层次问题，然后再深入推理。这种方法在多个推理基准测试上显著提升了PaLM-2L和GPT-4的性能。
Analogical Prompting is similar to SG-ICL, and automatically generates exemplars that include CoTs. It has demonstrated improvements in mathematical reasoning and code generation tasks.
类比提示 类似于自生成上下文学习（SG-ICL），能自动生成包含CoT的示例。它已在数学推理和代码生成任务中展现出改进效果。
Thread-of-Thought (ThoT) Prompting consists of an improved thought inducer for CoT reasoning. Instead of "Let's think step by step," it uses "Walk me through this context in manageable parts step by step, summarizing and analyzing as we go." This thought inducer works well in question-answering and retrieval settings, especially when dealing with large, complex contexts.
思维线程提示 包含一个改进的CoT推理思维诱导词。它不使用"让我们一步步思考"，而是使用"请一步步带我理解这个上下文，将其分解为可管理的部分，并边进行边总结和分析"。这个思维诱导词在问答和检索场景中效果良好，尤其是在处理大型复杂上下文时。
Tabular Chain-of-Thought (Tab-CoT) consists of a Zero-Shot CoT prompt that makes the LLM output reasoning as a markdown table. This tabular design enables the LLM to improve the structure and thus the reasoning of its output.
表格化思维链 是一种零样本CoT提示，它使LLM以Markdown表格形式输出推理过程。这种表格化设计有助于LLM改善输出结构，从而提升推理质量。
2.2.2.2 Few-Shot CoT
This set of techniques presents the LLM with multiple exemplars, which include chains-of-thought. This can significantly enhance performance. This technique is occasionally referred to as Manual-CoT or Golden CoT.
2.2.2.2 少样本思维链
这组技术向LLM提供多个包含思维链的示例。这能显著提升性能。该技术有时被称为人工CoT或Golden CoT。
Contrastive CoT Prompting adds both exemplars with incorrect and correct explanations to the CoT prompt in order to show the LLM how not to reason. This method has shown significant improvement in areas like Arithmetic Reasoning and Factual QA.
对比式CoT提示 向CoT提示中添加带有错误和正确解释的示例，以向LLM展示不应如何推理。该方法在算术推理和事实问答等领域展现出显著改进。
Uncertainty-Routed CoT Prompting samples multiple CoT reasoning paths, then selects the majority if it is above a certain threshold (calculated based on validation data). If not, it samples greedily and selects that response. This method demonstrates improvement on the MMLU benchmark for both GPT-4 and Gemini Ultra models.
不确定性路由CoT提示 采样多条CoT推理路径，如果多数路径的置信度超过某个阈值（基于验证数据计算），则选择该多数结果；否则，采用贪心采样并选择其响应。该方法在MMLU基准上对GPT-4和Gemini Ultra模型均有改进。
Complexity-based Prompting involves two major modifications to CoT. First, it selects complex examples for annotation and inclusion in the prompt, based on factors like question length or reasoning steps required. Second, during inference, it samples multiple reasoning chains (answers) and uses a majority vote among chains exceeding a certain length threshold, under the premise that longer reasoning indicates higher answer quality. This technique has shown improvements on three mathematical reasoning datasets.
基于复杂性的提示 包含两项对CoT的主要改进。首先，根据问题长度或所需推理步骤等因素，选择复杂示例进行标注并纳入提示。其次，在推理时，采样多条推理链（答案），并在超过一定长度阈值的链中使用多数投票，前提是更长的推理意味着更高的答案质量。该技术已在三个数学推理数据集上显示出改进。
Active Prompting starts with some training questions/exemplars, asks the LLM to solve them, then calculates uncertainty (disagreement in this case) and asks human annotators to rewrite the exemplars with highest uncertainty.
主动提示 从一些训练问题/示例开始，要求LLM解决它们，然后计算不确定性（此处指答案分歧），并请人工标注者重写不确定性最高的示例。
Memory-of-Thought Prompting leverage unlabeled training exemplars to build Few-Shot CoT prompts at test time. Before test time, it performs inference on the unlabeled training exemplars with CoT. At test time, it retrieves similar instances to the test sample. This technique has shown substantial improvements in benchmarks like Arithmetic, commonsense, and factual reasoning.
思维记忆提示 在测试时利用未标记的训练示例构建少样本CoT提示。测试前，使用CoT对未标记的训练示例进行推理。测试时，检索与测试样本相似的实例。该技术在算术、常识和事实推理等基准上取得了显著改进。
Automatic Chain-of-Thought (Auto-CoT) Prompting uses Wei et al. ’s Zero-Shot prompt to automatically generate chains of thought. These are then used to build a Few-Shot CoT prompt for a test sample.
自动思维链提示 使用Wei等人的零样本提示自动生成思维链，然后用这些思维链为测试样本构建少样本CoT提示。
2.2.3 Decomposition
Significant research has focused on decomposing complex problems into simpler sub-questions. This is an effective problem-solving strategy for humans as well as GenAI. Some decomposition techniques are similar to thought-inducing techniques, such as CoT, which often naturally breaks down problems into simpler components. However, explicitly breaking down problems can further improve LLMs' problem solving ability.
2.2.3 分解
大量研究聚焦于将复杂问题分解为更简单的子问题。这对人类和生成式AI来说都是一种有效的问题解决策略。一些分解技术与思维诱导技术（如CoT）相似，后者通常自然地将问题分解为更简单的组成部分。然而，显式地分解问题可以进一步提升LLM的问题解决能力。
Least-to-Most Prompting starts by prompting a LLM to break a given problem into sub-problems without solving them. Then, it solves them sequentially, appending model responses to the prompt each time, until it arrives at a final result. This method has shown significant improvements in tasks involving symbolic manipulation, compositional generalization, and mathematical reasoning.
由简入繁提示 首先提示LLM将给定问题分解为子问题而不求解。然后，依次求解这些子问题，每次将模型响应附加到提示中，直至得出最终结果。该方法在涉及符号操作、组合泛化和数学推理的任务中展现出显著改进。
Decomposed Prompting (DECOMP) Few-Shot prompts a LLM to show it how to use certain functions. These might include things like string splitting or internet searching; these are often implemented as separate LLM calls. Given this, the LLM breaks down its original problem into sub-problems which it sends to different functions. It has shown improved performance over Least-to-Most prompting on some tasks.
分解提示（DECOMP） 使用少样本提示向LLM展示如何使用某些函数。这些函数可能包括字符串拆分或互联网搜索，通常作为独立的LLM调用实现。基于此，LLM将其原始问题分解为子问题，并发送给不同的函数。在某些任务上，该方法表现出优于由简入繁提示的性能。
Plan-and-Solve Prompting consists of an improved Zero-Shot CoT prompt, "Let's first understand the problem and devise a plan to solve it. Then, let's carry out the plan and solve the problem step by step". This method generates more robust reasoning processes than standard Zero-Shot-CoT on multiple reasoning datasets.
规划求解提示 包含一个改进的零样本CoT提示："首先理解问题并制定解决方案，然后逐步执行计划并解决问题"。该方法在多个推理数据集上生成了比标准零样本CoT更稳健的推理过程。
Tree-of-Thought (ToT) creates a tree-like search problem by starting with an initial problem then generating multiple possible steps in the form of thoughts (as from a CoT). It evaluates the progress each step makes towards solving the problem (through prompting) and decides which steps to continue with, then keeps creating more thoughts. ToT is particularly effective for tasks that require search and planning.
思维树（ToT） 通过从一个初始问题开始，生成多个可能的思维步骤（类似于CoT），创建一个树状搜索问题。它评估每个步骤对解决问题的进展（通过提示），决定继续哪些步骤，然后继续生成更多思维。ToT对于需要搜索和规划的任务特别有效。
Recursion-of-Thought is similar to regular CoT. However, every time it encounters a complicated problem in the middle of its reasoning chain, it sends this problem into another prompt/LLM call. After this is completed, the answer is inserted into the original prompt. In this way, it can recursively solve complex problems, including ones which might otherwise run over that maximum context length. This method has shown improvements on arithmetic and algorithmic tasks. Though implemented using fine-tuning to output a special token that sends sub-problem into another prompt, it could also be done only through prompting.
递归思维 类似于常规CoT。然而，每当它在推理链中间遇到复杂问题时，就将该问题发送到另一个提示/LLM调用。完成后，答案被插入原始提示。这样，它可以递归求解复杂问题，包括那些可能超出最大上下文长度的问题。该方法在算术和算法任务上展现出改进。尽管实现时使用了微调来输出一个特殊token以将子问题发送到另一个提示，但也可以通过纯提示完成。
Program-of-Thoughts uses LLMs like Codex to generate programming code as reasoning steps. A code interpreter executes these steps to obtain the final answer. It excels in mathematical and programming-related tasks but is less effective for semantic reasoning tasks.
思维程序 使用类似Codex的LLM生成编程代码作为推理步骤。代码解释器执行这些步骤以获得最终答案。它在数学和编程相关任务上表现出色，但在语义推理任务上效果较差。
Faithful Chain-of-Thought generates a CoT that has both natural language and symbolic language (e.g. Python) reasoning, just like Program-of-Thoughts. However, it also makes use of different types of symbolic languages in a task-dependent fashion.
忠实思维链 生成包含自然语言和符号语言（例如Python）推理的CoT，类似于思维程序。然而，它还根据任务以不同方式使用不同类型的符号语言。
Skeleton-of-Thought focuses on accelerating answer speed through parallelization. Given a problem, it prompts an LLM to create a skeleton of the answer, in a sense, sub-problems to be solved. Then, in parallel, it sends these questions to a LLM and concatenates all the outputs to get a final response.
思维骨架 专注于通过并行化加速回答速度。给定一个问题，它提示LLM创建答案的骨架，从某种意义上说，就是待求解的子问题。然后，并行地将这些问题发送给LLM，并将所有输出拼接起来得到最终响应。
Metacognitive Prompting attempts to make the LLM mirror human metacognitive processes with a five part prompt chain, with steps including clarifying the question, preliminary judgement, evaluation of response, decision confirmation, and confidence assessment.
元认知提示 尝试让LLM镜像人类的元认知过程，通过一个包含五个部分的提示链，步骤包括澄清问题、初步判断、响应评估、决策确认和置信度评估。
2.2.4 Ensembling
In GenAI, ensembling is the process of using multiple prompts to solve the same problem, then aggregating these responses into a final output. In many cases, a majority vote—selecting the most frequent response—is used to generate the final output. Ensembling techniques reduce the variance of LLM outputs and often improving accuracy, but come with the cost of increasing the number of model calls needed to reach a final answer.
2.2.4 集成
在生成式AI中，集成是指使用多个提示来解决同一个问题，然后将这些响应聚合为最终输出的过程。在许多情况下，使用多数投票（选择最频繁出现的响应）来生成最终输出。集成技术降低了LLM输出的方差，并且通常能提高准确性，但其代价是增加了得出最终答案所需的模型调用次数。
Demonstration Ensembling (DENSE) creates multiple few-shot prompts, each containing a distinct subset of exemplars from the training set. Next, it aggregates over their outputs to generate a final response.
演示集成（DENSE） 创建多个少样本提示，每个提示包含来自训练集的不同子集示例。然后，聚合它们的输出来生成最终响应。
Mixture of Reasoning Experts (MoRE) creates a set of diverse reasoning experts by using different specialized prompts for different reasoning types (such as retrieval augmentation prompts for factual reasoning, Chain-of-Thought reasoning for multi-hop and math reasoning, and generated knowledge prompting for commonsense reasoning). The best answer from all experts is selected based on an agreement score.
推理专家混合（MoRE） 通过为不同推理类型使用不同的专门提示，创建一组多样化的推理专家（例如，用于事实推理的检索增强提示、用于多跳和数学推理的思维链提示，以及用于常识推理的生成知识提示）。根据一致性得分从所有专家中选择最佳答案。
Max Mutual Information Method creates multiple prompt templates with varied styles and exemplars, then selects the optimal template as the one that maximizes mutual information between the prompt and the LLM's outputs.
最大互信息法 创建具有不同风格和示例的多个提示模板，然后选择使提示与LLM输出之间互信息最大化的最优模板。
Self-Consistency is based on the intuition that multiple different reasoning paths can lead to the same answer. This method first prompts the LLM multiple times to perform CoT, crucially with a non-zero temperature to elicit diverse reasoning paths. Next, it uses a majority vote over all generated responses to select a final response. Self-Consistency has shown improvements on arithmetic, commonsense, and symbolic reasoning tasks.
自洽性 基于这样一个直觉：多个不同的推理路径可以导向相同的答案。该方法首先多次提示LLM执行CoT，关键在于使用非零温度以引出多样化的推理路径。然后，对所有生成的响应进行多数投票以选择最终响应。自洽性在算术、常识和符号推理任务上展现出改进。
Universal Self-Consistency is similar to Self-Consistency except that rather than selecting the majority response by programmatically counting how often it occurs, it inserts all outputs into a prompt template that selects the majority answer. This is helpful for free-form text generation and cases where the same answer may be output slightly differently by different prompts.
通用自洽性 类似于自洽性，区别在于它不是通过程序化计数来选择多数响应，而是将所有输出插入到一个选择多数答案的提示模板中。这对于自由形式的文本生成以及不同提示可能输出略有不同的相同答案的情况很有帮助。
Meta-Reasoning over Multiple CoTs is similar to universal Self-Consistency; it first generates multiple reasoning chains (but not necessarily final answers) for a given problem. Next, it inserts all of these chains in a single prompt template then generates a final answer from them.
多CoT的元推理 类似于通用自洽性；它首先针对给定问题生成多个推理链（但不一定是最终答案）。然后，将所有推理链插入到一个提示模板中，并从中生成最终答案。
DiVeRSe creates multiple prompts for a given problem then performs Self-Consistency for each, generating multiple reasoning paths. They score reasoning paths based on each step in them then select a final response.
DiVeRSe 为给定问题创建多个提示，然后对每个提示执行自洽性，生成多条推理路径。它们根据路径中的每一步对推理路径进行评分，然后选择最终响应。
Consistency-based Self-adaptive Prompting (COSP) constructs Few-Shot CoT prompts by running Zero-Shot CoT with Self-Consistency on a set of examples then selecting a high agreement subset of the outputs to be included in the final prompt as exemplars. It again performs Self-Consistency with this final prompt.
基于一致性的自适应提示（COSP） 通过在一组示例上运行带自洽性的零样本CoT来构建少样本CoT提示，然后选择输出中一致性高的子集作为示例包含在最终提示中。然后，它再次使用此最终提示执行自洽性。
Universal Self-Adaptive Prompting (USP) builds upon the success of COSP, aiming to make it generalizable to all tasks. USP makes use of unlabeled data to generate exemplars and a more complicated scoring function to select them. Additionally, USP does not use Self-Consistency.
通用自适应提示（USP） 建立在COSP的成功之上，旨在使其适用于所有任务。USP利用未标记数据生成示例，并使用更复杂的评分函数来选择它们。此外，USP不使用自洽性。
Prompt Paraphrasing transforms an original prompt by changing some of the wording, while still maintaining the overall meaning. It is effectively a data augmentation technique that can be used to generate prompts for an ensemble.
提示释义 通过改变原始提示的部分措辞来转换它，同时保持整体含义不变。这本质上是一种数据增强技术，可用于生成用于集成的提示。
2.2.5 Self-Criticism
When creating GenAI systems, it can be useful to have LLMs criticize their own outputs. This could simply be a judgement (e.g., is this output correct) or the LLM could be prompted to provide feedback, which is then used to improve the answer. Many approaches to generating and integrating self-criticism have been developed.
2.2.5 自我批评
在创建生成式AI系统时，让LLM批评自己的输出可能很有用。这可以仅仅是一个判断（例如，这个输出是否正确），也可以提示LLM提供反馈，然后用反馈来改进答案。目前已开发出多种生成和整合自我批评的方法。
Self-Calibration first prompts an LLM to answer a question. Then, it builds a new prompt that includes the question, the LLM's answer, and an additional instruction asking whether the answer is correct. This can be useful for gauging confidence levels when applying LLMs when deciding when to accept or revise the original answer.
自校准 首先提示LLM回答一个问题。然后，它构建一个新的提示，其中包含问题、LLM的答案以及一个询问答案是否正确的附加指令。这在应用LLM时判断置信水平，决定是接受还是修改原始答案时很有用。
Self-Refine is an iterative framework where, given an initial answer from the LLM, it prompts the same LLM to provide feedback on the answer, and then prompts the LLM to improve the answer based on the feedback. This iterative process continues until a stopping condition is met (e.g., max number of steps reached). Self-Refine has demonstrated improvement across a range of reasoning, coding, and generation tasks.
自我精炼 是一个迭代框架，在获得LLM的初始答案后，它提示同一个LLM对答案提供反馈，然后提示LLM根据反馈改进答案。这个迭代过程持续进行，直到满足停止条件（例如，达到最大步数）。自我精炼已在一系列推理、编码和生成任务上展现出改进。
Reversing Chain-of-Thought (RCoT) first prompts LLMs to reconstruct the problem based on generated answer. Then, it generates fine-grained comparisons between the original problem and the reconstructed problem as a way to check for any inconsistencies. These inconsistencies are then converted to feedback for the LLM to revise the generated answer.
逆向思维链（RCoT） 首先提示LLM根据生成的答案重构问题。然后，它在原始问题和重构问题之间生成细粒度的比较，以检查是否存在任何不一致。这些不一致随后被转化为反馈，供LLM修改生成的答案。
Self-Verification generates multiple candidate solutions with Chain-of-Thought (CoT). It then scores each solution by masking certain parts of the original question and asking an LLM to predict them based on the rest of the question and the generated solution. This method has shown improvement on eight reasoning datasets.
自我验证 使用思维链生成多个候选解决方案。然后，通过屏蔽原始问题的某些部分，并让LLM根据问题的其余部分和生成的解决方案来预测被屏蔽的部分，从而对每个解决方案进行评分。该方法已在八个推理数据集上显示出改进。
Chain-of-Verification (COVE) first uses an LLM to generate an answer to a given question. Then, it creates a list of related questions that would help verify the correctness of the answer. Each question is answered by the LLM, then all the information is given to the LLM to produce the final revised answer. This method has shown improvements in various question-answering and text-generation tasks.
验证链（COVE） 首先使用LLM为给定问题生成一个答案。然后，它创建一个有助于验证答案正确性的相关问题列表。LLM回答每个问题，然后将所有信息提供给LLM以生成最终修订后的答案。该方法已在各种问答和文本生成任务中展现出改进。
Cumulative Reasoning first generates several potential steps in answering the question. It then has a LLM evaluate them, deciding to either accept or reject these steps. Finally, it checks whether it has arrived at the final answer. If so, it terminates the process, but otherwise it repeats it. This method has demonstrated improvements in logical inference tasks and mathematical problem.
累积推理 首先生成回答问题的几个潜在步骤。然后让LLM评估这些步骤，决定接受或拒绝它们。最后，检查是否已得出最终答案。如果是，则终止过程，否则重复进行。该方法已在逻辑推理任务和数学问题上展现出改进。
2.3 Prompting Technique Usage
As we have just seen, there exist many text-based prompting techniques. However, only a small subset of them are commonly used in research and in industry. We measure technique usage by proxy of measuring the number of citations by other papers in our dataset. We do so with the presumption that papers about prompting are more likely to actually use or evaluate the cited technique. We graph the top 25 papers cited in this way from our dataset and find that most of them propose new prompting techniques (Figure 2.11). The prevalence of citations for Few-Shot and Chain-of-Thought prompting is unsurprising and helps to establish a baseline for understanding the prevalence of other techniques.
2.3 提示技术使用情况
正如我们所见，存在许多基于文本的提示技术。然而，其中只有一小部分在研究和工业中被普遍使用。我们通过衡量数据集中其他论文对某项技术的引用次数来间接衡量其使用情况。我们这样做的假设是，关于提示的论文更有可能实际使用或评估被引用的技术。我们对数据集中以此方式被引用次数最多的前25篇论文进行了图表分析，发现其中大多数提出了新的提示技术（图2.11）。少样本提示和思维链提示的引用普遍性不足为奇，这有助于为理解其他技术的普及程度建立一个基线。
2.3.1 Benchmarks
In prompting research, when researchers propose a new technique, they usually benchmark it across multiple models and datasets. This is important to prove the utility of the technique and examine how it transfers across models.
2.3.1 基准测试
在提示研究中，当研究人员提出一项新技术时，他们通常会在多个模型和数据集上对其进行基准测试。这对于证明该技术的效用以及检验其在不同模型间的可迁移性非常重要。
In order to make it easier for researchers proposing new techniques to know how to benchmark them, we quantitatively examine which models (Figure 2.9) and what benchmark datasets (Figure 2.10) are being used. Again, we measure usage by how many times papers in our dataset cite the benchmark datasets and models.
为了使提出新技术的研究人员更容易了解如何进行基准测试，我们定量分析了哪些模型（图2.9）和哪些基准数据集（图2.10）正在被使用。同样，我们通过衡量数据集中论文引用这些基准数据集和模型的次数来度量其使用情况。
To find which datasets and models are being used, we prompted GPT-4-1106-preview to extract any mentioned dataset or model from the body of papers in our dataset. After, we manually filtered out results that were not models or datasets. The citation counts were acquired by searching items from the finalized list on Semantic Scholar.
为了找出哪些数据集和模型正在被使用，我们提示 GPT-4-1106-preview 从我们数据集的论文正文中提取任何提到的数据集或模型。之后，我们手动过滤掉了不是模型或数据集的结果。引用次数是通过在 Semantic Scholar 上搜索最终列表中的项目获得的。
2.4 Prompt Engineering
In addition to surveying prompting techniques, we also review prompt engineering techniques, which are used to automatically optimize prompts. We discuss some techniques that use gradient updates, since the set of prompt engineering techniques is much smaller than that of prompting techniques.
2.4 提示工程
除了调研提示技术，我们还回顾了用于自动优化提示的提示工程技术。我们讨论了一些使用梯度更新的技术，因为提示工程技术的集合远小于提示技术。
Meta Prompting is the process of prompting a LLM to generate or improve a prompt or prompt template. This is often done without any scoring mechanism, using just a simple template (Figure 2.12). However, other works present more complex uses of meta-prompting, with multiple iterations and scoring mechanisms.
元提示 是指提示大语言模型生成或改进提示或提示模板的过程。这通常是在没有任何评分机制的情况下完成的，仅使用一个简单的模板（图 2.12）。然而，其他工作提出了更复杂的元提示用法，包括多次迭代和评分机制。
Improve the following prompt: {PROMPT}
Figure 2.12: A simple Meta Prompting template
改进以下提示：{提示}
图 2.12：一个简单的元提示模板
AutoPrompt uses a frozen LLM as well as a prompt template that includes some "trigger tokens", whose values are updated via backpropagation at training time. This is a version of soft-prompting.
自动提示 使用一个冻结的大语言模型以及一个包含某些"触发词元"的提示模板，这些触发词元的值在训练时通过反向传播更新。这是软提示的一种版本。
Automatic Prompt Engineer (APE) uses a set of exemplars to generate a Zero-Shot instruction prompt. It generates multiple possible prompts, scores them, then creates variations of the best ones (e.g. by using prompt paraphrasing). It iterates on this process until some desiderata are reached.
自动提示工程师（APE） 使用一组示例来生成零样本指令提示。它生成多个可能的提示，对其进行评分，然后对最佳提示进行变体创建（例如通过提示释义）。它迭代此过程，直到达到某些期望目标。
Gradientfree Instructional Prompt Search (GrIPS) is similar to APE, but uses a more complex set of operations including deletion, addition, swapping, and paraphrasing in order to create variations of a starting prompt.
无梯度指令提示搜索（GrIPS） 类似于自动提示工程师，但使用更复杂的操作集，包括删除、添加、交换和释义，以创建起始提示的变体。
Prompt Optimization with Textual Gradients (ProTeGi) is a unique approach to prompt engineering that improves a prompt template through a multi-step process. First, it passes a batch of inputs through the template, then passes the output, ground truth, and prompt into another prompt that criticizes the original prompt. It generates new prompts from these criticisms then uses a bandit algorithm to select one. ProTeGi demonstrates improvements over methods like APE and GrIPS.
基于文本梯度的提示优化（ProTeGi） 是一种独特的提示工程方法，通过多步过程改进提示模板。首先，它将一批输入通过模板，然后将输出、真实值和提示传入另一个提示中，该提示批评原始提示。它根据这些批评生成新的提示，然后使用赌博机算法选择一个。ProTeGi 显示出优于自动提示工程师和无梯度指令提示搜索等方法的表现。
RLPrompt uses a frozen LLM with an unfrozen module added. It uses this LLM to generate prompt templates, scores the templates on a dataset, and updates the unfrozen module using Soft Q-Learning. Interestingly, the method often selects grammatically nonsensical text as the optimal prompt template.
RLPrompt 使用一个冻结的大语言模型并添加一个未冻结的模块。它利用这个大语言模型生成提示模板，在数据集上对模板进行评分，并使用软 Q-learning 更新未冻结的模块。有趣的是，该方法经常选择语法上无意义的文本作为最优提示模板。
Dialogue-comprised Policy-gradient-based Discrete Prompt Optimization (DP2O) is perhaps the most complicated prompt engineering technique, involving reinforcement learning, a custom prompt scoring function, and conversations with an LLM to construct the prompt.
基于对话的策略梯度离散提示优化（DP2O） 可能是最复杂的提示工程技术，涉及强化学习、自定义提示评分函数以及与大型语言模型的对话来构建提示。
2.5 Answer Engineering
Answer engineering is the iterative process of developing or selecting among algorithms that extract precise answers from LLM outputs. To understand the need for answer engineering, consider a binary classification task where the labels are "Hate Speech" and "Not Hate Speech". The prompt template might look like this:
2.5 答案工程
答案工程是从大语言模型输出中提取精确答案的算法开发和选择的迭代过程。为了理解答案工程的必要性，考虑一个标签为"仇恨言论"和"非仇恨言论"的二分类任务。提示模板可能如下所示：
Is this "Hate Speech" or "Not Hate Speech":
{TEXT}
这是"仇恨言论"还是"非仇恨言论"：
{文本}
When a hate speech sample is put through the template, it might have outputs such as "It's hate speech", "Hate Speech.", or even "Hate speech, because it uses negative language against a racial group". This variance in response formats is difficult to parse consistently; improved prompting can help, but only to a certain extent.
当一个仇恨言论样本通过该模板时，它可能产生诸如"这是仇恨言论"、"仇恨言论。"甚至"仇恨言论，因为它对某个种族群体使用了负面语言"等输出。这种响应格式的差异性难以一致地解析；改进提示可以提供帮助，但效果有限。
There are three design decisions in answer engineering, the choice of answer space, answer shape, and answer extractor (Figure 2.13). Liu et al. (2023b) define the first two as necessary components of answer engineering and we append the third. We consider answer engineering to be distinct from prompt engineering, but extremely closely related; the processes are often conducted in tandem.
答案工程中有三个设计决策：答案空间、答案形状和答案提取器的选择。Liu等人将前两个定义为答案工程的必要组成部分，我们补充了第三个。我们认为答案工程与提示工程不同，但两者密切相关；这两个过程通常是同时进行的。
2.5.1 Answer Shape
The shape of an answer is its physical format. For example, it could be a token, span of tokens, or even an image or video. It is sometimes useful to restrict the output shape of a LLM to a single token for tasks like binary classification.
2.5.1 答案形状
答案的形状是其物理格式。例如，它可以是一个词元、一段词元序列，甚至是图像或视频。在像二分类这样的任务中，有时将大语言模型的输出形状限制为单个词元是很有用的。
2.5.2 Answer Space
The space of an answer is the domain of values that its structure may contain. This may simply be the space of all tokens, or in a binary labeling task, could just be two possible tokens.
2.5.2 答案空间
答案空间是其结构可能包含的值的域。这可能是所有词元的空间，或者在一个二分类标注任务中，可能只是两个可能的词元。
2.5.3 Answer Extractor
In cases where it is impossible to entirely control the answer space (e.g. consumer-facing LLMs), or the expected answer may be located somewhere within the model output, a rule can be defined to extract the final answer. This rule is often a simple function (e.g. a regular expression), but can also use a separate LLM to extract the answer.
2.5.3 答案提取器
在无法完全控制答案空间（例如面向消费者的大语言模型）的情况下，或者预期答案可能位于模型输出的某个位置时，可以定义一个规则来提取最终答案。这个规则通常是一个简单的函数（例如正则表达式），但也可以使用单独的大语言模型来提取答案。
Verbalizer Often used in labeling tasks, a verbalizer maps a token, span, or other type of output to a label and vice-versa (injective). For example, if we wish for a model to predict whether a Tweet is positive or negative, we could prompt it to output either "+" or "-" and a verbalizer would map these token sequences to the appropriate labels. The selection of a verbalizer constitutes a component of answer engineering.
语言化器 常用于标注任务，语言化器将词元、词元段或其他类型的输出映射到标签，反之亦然（单射）。例如，如果我们希望模型预测一条推文是正面还是负面，我们可以提示它输出"+"或"-"，然后语言化器将这些词元序列映射到相应的标签。语言化器的选择构成了答案工程的一个组成部分。
Regex As mentioned previously, Regexes are often used to extract answers. They are usually used to search for the first instance of a label. However, depending on the output format and whether CoTs are generated, it may be better to search for the last instance.
正则表达式 如前所述，正则表达式常用于提取答案。它们通常用于搜索标签的第一个实例。然而，根据输出格式以及是否生成思维链，搜索最后一个实例可能更好。
Separate LLM Sometimes outputs are so complicated that regexes won't work consistently. In this case, it can be useful to have a separate LLM evaluate the output and extract an answer. This separate LLM will often use an answer trigger, e.g. "The answer (Yes or No) is", to extract the answer.
独立大语言模型 有时输出过于复杂，正则表达式无法稳定工作。在这种情况下，使用独立的大语言模型评估输出并提取答案可能很有用。这个独立的大语言模型通常会使用一个答案触发器，例如"答案是（是或否）："，来提取答案。
Beyond English Text Prompting
Prompting GenAIs with English text currently stands as the dominant method for interaction. Prompting in other languages or through different modalities often requires special techniques to achieve comparable performance. In this context, we discuss the domains of multilingual and multimodal prompting.
使用英语文本提示生成式AI目前是主流的交互方式。用其他语言或通过不同模态进行提示，通常需要特殊技术才能达到相当的性能。在此背景下，我们讨论多语言和多模态提示的领域。
3.1 Multilingual
State-of-the-art GenAIs have often been predominately trained with English dataset, leading to a notable disparity in the output quality in languages other than English, particularly low-resource languages. As a result, various multilingual prompting techniques have emerged in an attempt to improve model performance in non-English settings (Figure 3.1).
多语言
最先进的生成式AI通常主要使用英语数据集进行训练，导致在英语以外的语言（尤其是低资源语言）的输出质量上存在显著差异。因此，出现了各种多语言提示技术，试图提高模型在非英语环境下的性能。
Translate First Prompting is perhaps the simplest strategy and first translates non-English input examples into English. By translating the inputs into English, the model can utilize its strengths in English to better understand the content. Translation tools vary; Shi et al. use an external MT system, Etxaniz et al. prompt multilingual LMs and Awasthi et al. prompt LLMs to translate non-English inputs.
先翻译后提示 可能是最简单的策略，它首先将非英语输入示例翻译成英语。通过将输入翻译成英语，模型可以利用其在英语上的优势来更好地理解内容。翻译工具各不相同；有些使用外部机器翻译系统，有些提示多语言LM，还有些提示LLM来翻译非英语输入。
3.1.1 Chain-of-Thought (CoT)
CoT prompting has been extended to the multilingual setting in multiple ways.
3.1.1 思维链
思维链提示已被扩展到多语言环境中，有多种方式。
XLT (Cross-Lingual Thought) Prompting utilizes a prompt template composed of six separate instructions, including role assignment, cross-lingual thinking, and CoT.
跨语言思维提示 利用由六个独立指令组成的提示模板，包括角色分配、跨语言思考和思维链。
Cross-Lingual Self Consistent Prompting (CLSP) introduces an ensemble technique that constructs reasoning paths in different languages to answer the same question.
跨语言自洽提示 引入了一种集成技术，用不同语言构建推理路径来回答同一个问题。
3.1.2 In-Context Learning
ICL has also been extended to multilingual settings in multiple ways.
3.1.2 上下文学习
上下文学习也已通过多种方式扩展到多语言环境。
X-InSTA Prompting explores three distinct approaches for aligning in-context examples with the input sentence for classification tasks: using semantically similar examples to the input (semantic alignment), examples that share the same label as the input (task-based alignment), and the combination of both semantic and task-based alignments.
X-InSTA提示 探索了三种不同的方法，用于在分类任务中将上下文示例与输入句子对齐：使用与输入语义相似的示例（语义对齐）、与输入共享相同标签的示例（基于任务的对齐），以及语义和基于任务的对齐相结合。
In-CLT (Cross-lingual Transfer) Prompting leverages both the source and target languages to create in-context examples, diverging from the traditional method of using source language exemplars. This strategy helps stimulate the cross-lingual cognitive capabilities of multilingual LLMs, thus boosting performance on cross-lingual tasks.
跨语言迁移提示 利用源语言和目标语言来创建上下文示例，这与使用源语言示例的传统方法不同。这种策略有助于激发多语言大语言模型的跨语言认知能力，从而提升跨语言任务的性能。
3.1.2.1 In-Context Example Selection
In-context example selection heavily influences the multilingual performance of LLMs. Finding in-context examples that are semantically similar to the source text is very important. However, using semantically dissimilar (peculiar) exemplars has also been shown to enhance performance. This same contrast exists in the English-only setting. Additionally, when dealing with ambiguous sentences, selecting exemplars with polysemous or rare word senses may boost performance.
3.1.2.1 上下文示例选择
上下文示例的选择对LLM的多语言性能有很大影响。找到与源文本语义相似的上下文示例非常重要。然而，使用语义不相似的（奇特的）示例也被证明可以提升性能。这种对比在纯英语环境中也存在。此外，在处理歧义句子时，选择具有多义词或罕见词义的示例可能会提高性能。
PARC (Prompts Augmented by Retrieval Cross-lingually) introduce a framework that retrieves relevant exemplars from a high resource language. This framework is specifically designed to enhance cross-lingual transfer performance, particularly for low-resource target languages. extend this work to Bangla.
跨语言检索增强提示 引入了一个从高资源语言中检索相关示例的框架。该框架专门设计用于增强跨语言迁移性能，特别是对于低资源的目标语言。该工作被扩展到孟加拉语。
3.1.3 Prompt Template Language Selection
In multilingual prompting, the selection of language for the prompt template can markedly influence the model performance.
3.1.3 提示模板语言选择
在多语言提示中，提示模板的语言选择会显著影响模型性能。
English Prompt Template Constructing the prompt template in English is often more effective than in the task language for multilingual tasks. This is likely due to the predominance of English data during LLM pre-training. suggest that this is likely due to a high overlap with pre-training data and vocabulary. Similarly, highlight how translation errors when creating task language templates propagate in the form of incorrect syntax and semantics, adversely affecting task performance. Further, compare in-lingual (task language) prompts and cross-lingual (mixed language) prompts and find the cross-lingual approach to be more effective, likely because it uses more English in the prompt, thus facilitating retrieving knowledge from the model.
英语提示模板 在多语言任务中，使用英语构建提示模板通常比使用任务语言更有效。这可能是因为LLM预训练期间英语数据占主导地位。有观点认为这可能是由于与预训练数据和词汇高度重叠所致。类似地，也有人指出创建任务语言模板时的翻译错误会以不正确的语法和语义形式传播，从而对任务性能产生不利影响。进一步地，比较语内提示（任务语言）和跨语提示（混合语言）发现，跨语方法更有效，可能是因为它在提示中使用了更多英语，从而促进了从模型中检索知识。
Task Language Prompt Template In contrast, many multilingual prompting benchmarks such as BUFFET or LongBench use task language prompts for language-specific use cases. specifically studies different translation methods when constructing native-language prompts. They demonstrate that human translated prompts are superior to their machine-translated counterparts. Native or non-native template performance can differ across tasks and models. As such, neither option will always be the best approach.
任务语言提示模板 相比之下，许多多语言提示基准（如BUFFET或LongBench）在特定语言用例中使用任务语言提示。有研究专门探讨了构建母语提示时的不同翻译方法，表明人工翻译的提示优于机器翻译的提示。母语或非母语模板的性能可能因任务和模型而异。因此，这两种选择并不总是最优的。
3.1.4 Prompting for Machine Translation
There is significant research into leveraging GenAI to facilitate accurate and nuanced translation. Although this is a specific application of prompting, many of these techniques are important more broadly for multilingual prompting.
3.1.4 机器翻译的提示
有大量研究致力于利用生成式AI实现准确且细致的翻译。虽然这是提示的一个特定应用，但这些技术中的许多对于多语言提示具有更广泛的重要意义。
Multi-Aspect Prompting and Selection (MAPS) mimics the human translation process, which involves multiple preparatory steps to ensure high-quality output. This framework starts with knowledge mining from the source sentence (extracting keywords and topics, and generating translation exemplars). It integrates this knowledge to generate multiple possible translations, then selects the best one.
多角度提示与选择（MAPS） 模仿人类翻译过程，涉及多个准备步骤以确保高质量的输出。该框架首先从源句中进行知识挖掘（提取关键词和主题，并生成翻译示例）。它整合这些知识以生成多个可能的翻译，然后选择最佳的一个。
Chain-of-Dictionary (CoD) first extracts words from the source phrase, then makes a list of their meanings in multiple languages, automatically via retrieval from a dictionary (e.g. English: 'apple', Spanish: 'manzana'). Then, they prepend these dictionary phrases to the prompt, where it asks a GenAI to use them during translation.
词典链（CoD） 首先从源短语中提取单词，然后通过从词典中自动检索，列出这些单词在多种语言中的含义（例如英语："apple"，西班牙语："manzana"）。然后，将这些词典短语添加到提示前面，要求生成式AI在翻译过程中使用它们。
Dictionary-based Prompting for Machine Translation (DiPMT) works similarly to CoD, but only gives definitions in the source and target languages, and formats them slightly differently.
基于词典的机器翻译提示（DiPMT） 工作原理与CoD类似，但只提供源语言和目标语言的定义，并且格式略有不同。
Decomposed Prompting for MT (DecoMT) divides the source text into several chunks and translates them independently using few-shot prompting. Then it uses these translations and contextual information between chunks to generate a final translation.
分解式机器翻译提示（DecoMT） 将源文本分成几个块，并使用少样本提示独立翻译它们。然后利用这些翻译和块之间的上下文信息生成最终翻译。
3.1.4.1 Human-in-the-Loop
Interactive-Chain-Prompting (ICP) deals with potential ambiguities in translation by first asking the GenAI to generate sub-questions about any ambiguities in the phrase to be translated. Humans later respond to these questions and the system includes this information to generate a final translation.
3.1.4.1 人在回路
交互式链式提示（ICP） 通过首先生成式AI生成关于待翻译短语中任何歧义的子问题来处理翻译中的潜在歧义。随后人类回答这些问题，系统在生成最终翻译时纳入此信息。
Iterative Prompting also involves humans during translation. First, they prompt LLMs to create a draft translation. This initial version is further refined by integrating supervision signals obtained from either automated retrieval systems or direct human feedback.
迭代提示 在翻译过程中也涉及人类。首先，他们提示LLM创建翻译草稿。然后通过整合从自动检索系统或直接人类反馈获得的监督信号，对初始版本进行进一步优化。
3.2 Multimodal
As GenAI models evolve beyond text-based domains, new prompting techniques emerge. These multimodal prompting techniques are often not simply applications of text-based prompting techniques, but entirely novel ideas made possible by different modalities. We now extend our text-based taxonomy to include a mixture of multimodal analogs of text-based prompting techniques as well as completely novel multimodal techniques (Figure 3.2).
3.2 多模态
随着生成式AI模型超越基于文本的领域，新的提示技术不断涌现。这些多模态提示技术往往不是简单地应用基于文本的提示技术，而是由不同模态带来的全新想法。我们现在扩展基于文本的分类法，既包括基于文本的提示技术的多模态类比，也包括全新的多模态技术。
3.2.1 Image Prompting
The image modality encompasses data such as photographs, drawings, or even screenshots of text. Image prompting may refer to prompts that either contain images or are used to generate images. Common tasks include image generation, caption generation, image classification, and image editing. We now describe various image prompting techniques used for such applications.
3.2.1 图像提示
图像模态涵盖照片、绘图甚至文本截图等数据。图像提示可以指包含图像的提示，也可以指用于生成图像的提示。常见任务包括图像生成、标题生成、图像分类和图像编辑。我们现在描述用于此类应用的各种图像提示技术。
Prompt Modifiers are simply words appended to a prompt to change the resultant image. Components such as Medium (e.g. "on canvas") or Lighting (e.g. "a well lit scene") are often used.
提示修饰符 就是附加到提示中以改变生成图像的词语。诸如媒介（例如"在画布上"）或光照（例如"光线充足的场景"）等组件经常被使用。
Negative Prompting allows users to numerically weight certain terms in the prompt so that the model considers them more/less heavily than others. For example, by negatively weighting the terms "bad hands" and "extra digits", models may be more likely to generate anatomically accurate hands.
负向提示 允许用户在提示中对某些术语进行数值加权，使模型对它们的考虑程度高于/低于其他术语。例如，通过对"糟糕的手"和"多余的手指"这些术语进行负向加权，模型可能更有可能生成解剖学上准确的手。
3.2.1.1 Multimodal In-Context Learning
The success of ICL in text-based settings has prompted research into multimodal ICL.
3.2.1.1 多模态上下文学习
上下文学习在基于文本环境中的成功激发了人们对多模态上下文学习的研究。
Paired-Image Prompting shows the model two images: one before and one after some transformation. Then, present the model with a new image for which it will perform the demonstrated conversion. This can be done either with textual instructions or without them.
配对图像提示 向模型展示两张图像：一张在某种变换之前，一张在变换之后。然后，向模型展示一张新图像，它将执行演示的转换。这可以带有文本指令，也可以不带。
Image-as-Text Prompting generates a textual description of an image. This allows for the easy inclusion of the image (or multiple images) in a text-based prompt.
图像即文本提示 生成图像的文本描述。这允许将图像（或多张图像）轻松包含在基于文本的提示中。
3.2.2 Audio Prompting
Prompting has also been extended to the audio modality. Experiments with audio ICL have generated mixed results, with some open source audio models failing to perform ICL. However, other results do show an ICL ability in audio models. Audio prompting is currently in early stages, but we expect to see various prompting techniques proposed in the future.
3.2.2 音频提示
提示也已扩展到音频模态。音频上下文学习的实验结果喜忧参半，一些开源音频模型无法执行上下文学习。然而，其他结果确实显示了音频模型具有上下文学习能力。音频提示目前处于早期阶段，但我们预计未来会出现各种提示技术。
3.2.3 Video Prompting
Prompting has also been extended to the video modality, for use in text-to-video generation, video editing, and video-to-text generation.
3.2.3 视频提示
提示也已扩展到视频模态，用于文本到视频生成、视频编辑和视频到文本生成。
3.2.3.1 Video Generation Techniques
When prompting a model to generate video, various modalities of prompts can be used as input, and several prompt-related techniques are often employed to enhance video generation. Image related techniques, such as prompt modifiers can often be used for video generation.
3.2.3.1 视频生成技术
在提示模型生成视频时，可以使用各种模态的提示作为输入，并且通常采用几种与提示相关的技术来增强视频生成。图像相关技术（例如提示修饰符）通常可用于视频生成。
3.2.4 Segmentation Prompting
Prompting can also be used for segmentation (e.g. semantic segmentation).
3.2.4 分割提示
提示也可用于分割（例如语义分割）。
3.2.5 3D Prompting
Prompting can also be used in 3D modalities, for example in 3D object synthesis, 3D surface texturing, and 4D scene generation (animating a 3D scene), where input prompt modalities include text, image, user annotation (bounding boxes, points, lines), and 3D objects.
3.2.5 3D提示
提示也可用于3D模态，例如在3D对象合成、3D表面纹理化和4D场景生成（将3D场景动画化）中，输入提示模态包括文本、图像、用户注释（边界框、点、线）和3D对象。
Extensions of Prompting
The techniques we have discussed thus far can be extremely complicated, incorporating many steps and iterations. However, we can take prompting further by adding access to external tools (agents) and complex evaluation algorithms to judge the validity of LLM outputs.
到目前为止我们讨论的技术可能极其复杂，包含许多步骤和迭代。然而，我们还可以通过增加对外部工具（智能体）的访问以及复杂的评估算法来判断LLM输出的有效性，从而将提示技术推进一步。
4.1 Agents
As LLMs have improved rapidly in capabilities, companies and researchers have explored how to allow them to make use of external systems. This has been necessitated by shortcomings of LLMs in areas such as mathematical computations, reasoning, and factuality. This has driven significant innovations in prompting techniques; these systems are often driven by prompts and prompt chains, which are heavily engineered to allow for agent-like behaviour (Figure 4.1).
4.1 智能体
随着LLM能力的快速提升，公司研究人员一直在探索如何让它们利用外部系统。LLM在数学计算、推理和事实性等方面的缺陷使得这一需求成为必然。这推动了提示技术的重大创新；这些系统通常由提示和提示链驱动，经过大量工程化设计以实现类似智能体的行为。
Definition of Agent In the context of GenAI, we define agents to be GenAI systems that serve a user's goals via actions that engage with systems outside the GenAI itself. This GenAI is usually a LLM. As a simple example, consider an LLM that is tasked with solving the following math problem:
智能体的定义 在生成式AI的背景下，我们将智能体定义为通过与非生成式AI系统本身之外的系统进行交互来服务用户目标的生成式AI系统。这个生成式AI通常是一个LLM。一个简单的例子是，考虑一个LLM被要求解决以下数学问题：
If Annie has 4,939 grapes, and gives exactly 39% of them to Amy, how many does she have left?
如果安妮有4,939颗葡萄，并将其中39%给了艾米，她还剩下多少颗？
If properly prompted, the LLM could output the string CALC(4,939*.39). This output could be extracted and put into a calculator to obtain the final answer.
如果提示得当，LLM可以输出字符串 CALC(4,939*.39)。这个输出可以被提取出来并放入计算器以获得最终答案。
This is an example of an agent: the LLM outputs text which then uses a downstream tool. Agent LLMs may involve a single external system (as above), or they may need to solve the problem of routing, to choose which external system to use. Such systems also frequently involve memory and planning in addition to actions.
这是一个智能体的例子：LLM输出文本，然后该文本使用下游工具。智能体LLM可能涉及单个外部系统（如上所述），也可能需要解决路由问题，即选择使用哪个外部系统。除了行动之外，此类系统通常还涉及记忆和规划。
Examples of agents include LLMs that can make API calls to use external tools like a calculator, LLMs that can output strings that cause actions to be taken in a gym-like environment, and more broadly, LLMs which write and record plans, write and run code, search the internet, and more. OpenAI Assistants, LangChain Agents, and LlamaIndex Agents are additional examples.
智能体的例子包括：可以通过API调用来使用计算器等外部工具的LLM；可以输出字符串以在类似gym的环境中触发行动的LLM；以及更广泛地，可以编写和记录计划、编写和运行代码、搜索互联网等的LLM。OpenAI Assistants、LangChain Agents和LlamaIndex Agents是另外的例子。
4.1.1 Tool Use Agents
Tool use is a critical component for GenAI agents. Both symbolic (e.g. calculator, code interpreter) and neural (e.g. a separate LLM) external tools are commonly used. Tools may occasionally be referred to as experts or modules.
4.1.1 工具使用智能体
工具使用是生成式AI智能体的关键组成部分。通常使用符号型（例如计算器、代码解释器）和神经型（例如一个独立的LLM）外部工具。工具有时也被称为专家或模块。
Modular Reasoning, Knowledge, and Language (MRKL) System is one of the simplest formulations of an agent. It contains a LLM router providing access to multiple tools. The router can make multiple calls to get information such as weather or the current date. It then combines this information to generate a final response. Toolformer, Gorilla, Act-1, and others all propose similar techniques, most of which involve some fine-tuning.
模块化推理、知识和语言系统 是最简单的智能体形式之一。它包含一个LLM路由器，提供对多个工具的访问。路由器可以进行多次调用以获取诸如天气或当前日期等信息。然后它将这些信息结合起来生成最终响应。Toolformer、Gorilla、Act-1以及其他类似技术都被提出，其中大部分涉及一些微调。
Self-Correcting with Tool-Interactive Critiquing (CRITIC) first generates a response to the prompt, with no external calls. Then, the same LLM criticizes this response for possible errors. Finally, it uses tools (e.g. Internet search or a code interpreter) accordingly to verify or amend parts of the response.
带工具交互评论的自我纠正 首先生成对提示的响应，不进行任何外部调用。然后，同一个LLM对这个响应进行评论，找出可能的错误。最后，相应地使用工具（例如互联网搜索或代码解释器）来验证或修正响应的部分内容。
4.1.2 Code-Generation Agents
Writing and executing code is another important ability of many agents.
4.1.2 代码生成智能体
编写和执行代码是许多智能体的另一项重要能力。
Program-aided Language Model (PAL) translates a problem directly into code, which is sent to a Python interpreter to generate an answer.
程序辅助语言模型 直接将问题翻译成代码，然后将代码发送给Python解释器以生成答案。
Tool-Integrated Reasoning Agent (ToRA) is similar to PAL, but instead of a single code generation step, it interleaves code and reasoning steps for as long as necessary to solve the problem.
工具集成推理智能体 类似于程序辅助语言模型，但不是单一的代码生成步骤，而是根据需要将代码和推理步骤交错进行以解决问题。
Task Weaver is also similar to PAL, transforming user requests into code, but can also make use of user-defined plugin.
Task Weaver 也类似于程序辅助语言模型，将用户请求转换为代码，但还可以利用用户定义的插件。
4.1.3 Observation-Based Agents
Some agents are designed to solve problems by interacting with toy environments. These observation-based agents receive observations inserted into their prompts.
4.1.3 基于观察的智能体
一些智能体旨在通过与模拟环境交互来解决问题。这些基于观察的智能体接收插入到其提示中的观察结果。
Reasoning and Acting (ReAct) generates a thought, takes an action, and receives an observation (and repeats this process) when given a problem to solve. All of this information is inserted into the prompt so it has a memory of past thoughts, actions, and observations.
ReAct 在给定待解问题时，生成一个想法，采取一个行动，并接收一个观察结果（并重复此过程）。所有这些信息都被插入到提示中，使其拥有过去想法、行动和观察的记忆。
Reflexion builds on ReAct, adding a layer of introspection. It obtains a trajectory of actions and observations, then is given an evaluation of success/failure. Then, it generates a reflection on what it did and what went wrong. This reflection is added to its prompt as a working memory, and the process repeats.
Reflexion 建立在ReAct的基础上，增加了一层内省。它获取行动和观察的轨迹，然后获得成功/失败的评估。接着，它对自己做了什么以及哪里出错了进行反思。这个反思作为工作记忆添加到其提示中，然后重复该过程。
4.1.3.1 Lifelong Learning Agents
Work on LLM-integrated Minecraft agents has generated impressive results, with agents able to acquire new skills as they navigate the world of this open-world videogame. We view these agents not merely as applications of agent techniques to Minecraft, but rather novel agent frameworks which can be explored in real world tasks that require lifelong learning.
4.1.3.1 终身学习智能体
关于LLM集成的Minecraft智能体的工作已经产生了令人印象深刻的结果，智能体在探索这个开放世界视频游戏时能够获得新技能。我们认为这些智能体不仅仅是代理技术在Minecraft中的应用，而是可以在需要终身学习的现实世界任务中探索的新型智能体框架。
Voyager is composed of three parts. First, it proposes tasks for itself to complete in order to learn more about the world. Second, it generates code to execute these actions. Finally, it saves these actions to be retrieved later when useful, as part of a long-term memory system. This system could be applied to real world tasks where an agent needs to explore and interact with a tool or website (e.g. penetration testing, usability testing).
Voyager 由三部分组成。首先，它为自己提出要完成的任务，以便更多地了解世界。其次，它生成代码来执行这些动作。最后，它将这些动作保存起来，以便将来有用时检索，作为长期记忆系统的一部分。该系统可以应用于需要智能体探索工具或网站并与之交互的现实世界任务（例如渗透测试、可用性测试）。
Ghost in the Minecraft (GITM) starts with an arbitrary goal, breaks it down into subgoals recursively, then iteratively plans and executes actions by producing structured text (e.g. "equip(sword)") rather than writing code. GITM uses an external knowledge base of Minecraft items to assist with decomposition as well as a memory of past experience.
Ghost in the Minecraft (GITM) 从一个任意目标开始，递归地将其分解为子目标，然后通过生成结构化文本（例如"equip(sword)"）而不是编写代码来迭代地规划和执行动作。GITM使用Minecraft物品的外部知识库来协助分解，并利用过去经验的记忆。
4.1.4 Retrieval Augmented Generation (RAG)
In the context of GenAI agents, RAG is a paradigm in which information is retrieved from an external source and inserted into the prompt. This can enhance performance in knowledge intensive tasks. When retrieval itself is used as an external tool, RAG systems are considered to be agents.
4.1.4 检索增强生成 (RAG)
在生成式AI智能体的背景下，RAG是一种从外部来源检索信息并将其插入提示中的范式。这可以提升知识密集型任务的性能。当检索本身被用作外部工具时，RAG系统被视为智能体。
Verify-and-Edit improves on self-consistency by generating multiple chains-of-thought, then selecting some to be edited. They do this by retrieving relevant (external) information to the CoTs, and allowing the LLM to augment them accordingly.
验证与编辑 通过生成多条思维链，然后选择其中一些进行编辑来改进自洽性。它们通过检索与思维链相关的外部信息，并允许LLM相应地增强这些思维链来实现这一点。
Demonstrate-Search-Predict first decomposes a question into sub-questions, then uses queries to solve them and combine their responses in a final answer. It uses few-shot prompting to decompose the problem and combine responses.
演示-搜索-预测 首先将问题分解为子问题，然后使用查询来解决这些子问题，并将它们的响应组合成最终答案。它使用少样本提示来分解问题并组合响应。
Interleaved Retrieval guided by Chain-of-Thought (IRCoT) is a technique for multi-hop question answering that interleaves CoT and retrieval. IRCoT leverages CoT to guide which documents to retrieve and retrieval to help plan the reasoning steps of CoT.
思维链引导的交错检索 是一种用于多跳问答的技术，它将思维链和检索交错进行。该技术利用思维链来指导检索哪些文档，并利用检索来帮助规划思维链的推理步骤。
Iterative Retrieval Augmentation techniques, like Forward-Looking Active REtrieval augmented generation (FLARE) and Imitate, Retrieve, Paraphrase (IRP), perform retrieval multiple times during long-form generation. Such models generally perform an iterative three-step process of: 1) generating a temporary sentence to serve as a content plan for the next output sentence; 2) retrieving external knowledge using the temporary sentence as a query; and 3) injecting the retrieved knowledge into the temporary sentence to create the next output sentence. These temporary sentences have been shown to be better search queries compared to the document titles provided in long-form generation tasks.
迭代检索增强 技术，例如前向主动检索增强生成和模仿-检索-释义，在长文本生成过程中多次执行检索。此类模型通常执行一个迭代的三步过程：1）生成一个临时句子，作为下一个输出句子的内容计划；2）使用该临时句子作为查询来检索外部知识；3）将检索到的知识注入临时句子以创建下一个输出句子。这些临时句子已被证明在长文本生成任务中比提供的文档标题是更好的搜索查询。
4.2 Evaluation
The potential of LLMs to extract and reason about information and understand user intent makes them strong contenders as evaluators. For example, it is possible to prompt a LLM to evaluate the quality of an essay or even a previous LLM output according to some metrics defined in the prompt. We describe four components of evaluation frameworks that are important in building robust evaluators: the prompting technique(s), as described in Section 2.2, the output format of the evaluation, the framework of the evaluation pipeline, and some other methodological design decisions (Figure 4.2).
4.2 评估
LLM在提取信息、推理信息和理解用户意图方面的潜力，使其成为强有力的评估者候选。例如，可以提示一个LLM根据提示中定义的某些指标来评估一篇文章的质量，甚至是之前LLM的输出。我们描述了构建稳健评估者时重要的四个评估框架组成部分：提示技术（如第2.2节所述）、评估的输出格式、评估流程的框架以及其他一些方法论设计决策（图4.2）。
4.2.1 Prompting Techniques
The prompting technique used in the evaluator prompt (e.g. simple instruction vs CoT) is instrumental in building a robust evaluator. Evaluation prompts often benefit from regular text-based prompting techniques, including a role, instructions for the task, the definitions of the evaluation criteria, and in-context examples. Find a full list of techniques in Appendix A.6.
4.2.1 提示技术
评估者提示中使用的提示技术（例如简单指令与思维链）对于构建稳健的评估者至关重要。评估提示通常受益于常规的基于文本的提示技术，包括角色、任务指令、评估标准的定义以及上下文示例。完整的技术列表见附录A.6。
In-Context Learning is frequently used in evaluation prompts, much in the same way it is used in other applications.
上下文学习 在评估提示中经常被使用，其方式与在其他应用中的使用方式大致相同。
Role-based Evaluation is a useful technique for improving and diversifying evaluations. By creating prompts with the same instructions for evaluation, but different roles, it is possible to effectively generate diverse evaluations. Additionally, roles can be used in a multiagent setting where LLMs debate the validity of the text to be evaluated.
基于角色的评估 是改进评估并使评估多样化的有用技术。通过创建具有相同评估指令但不同角色的提示，可以有效地生成多样化的评估。此外，角色可以在多智能体设置中使用，让LLM就待评估文本的有效性进行辩论。
Chain-of-Thought prompting can further improve evaluation performance.
思维链 提示可以进一步提升评估性能。
Model-Generated Guidelines prompt an LLM to generate guidelines for evaluation. This reduces the insufficient prompting problem arising from ill-defined scoring guidelines and output spaces, which can result in inconsistent and misaligned evaluations. One approach generates a chain-of-thought of the detailed evaluation steps that the model should perform before generating a quality assessment. Another approach, AUTOCALIBRATE, derives scoring criteria based on expert human annotations and uses a refined subset of model-generated criteria as a part of the evaluation prompt.
模型生成指南 提示LLM生成评估指南。这减少了因评分指南和输出空间定义不明确而导致的提示不足问题，该问题可能导致评估不一致和错位。一种方法是让模型在进行质量评估之前，生成一个包含详细评估步骤的思维链。另一种方法AUTOCALIBRATE基于专家人工标注推导评分标准，并使用模型生成标准的精炼子集作为评估提示的一部分。
4.2.2 Output Format
The output format of the LLM can significantly affect evaluation performance.
4.2.2 输出格式
LLM的输出格式会显著影响评估性能。
Styling Formatting the LLM's response using XML or JSON styling has also been shown to improve the accuracy of the judgment generated by the evaluator.
样式化 使用XML或JSON样式格式化LLM的响应也被证明可以提高评估者生成的判断的准确性。
Linear Scale A very simple output format is a linear scale (e.g. 1-5). Many works use ratings of 1-10, 1-5, or even 0-1. The model can be prompted to output a discrete or continuous score between the bounds.
线性量表 一种非常简单的输出格式是线性量表。许多研究使用1-10、1-5甚至0-1的评分。可以提示模型在边界之间输出离散或连续的分数。
Score the following story on a scale of 1-5 from well to poorly written:
{INPUT}
请从写得好到写得差按1-5分给以下故事评分：
{输入}
Binary Score Prompting the model to generate binary responses like Yes or No and True or False is another frequently used output format.
二元评分 提示模型生成二元响应，如"是"或"否"以及"真"或"假"，是另一种常用的输出格式。
Is the following story well written at a high-school level (yes/no)?:
{INPUT}
以下故事是否符合高中水平的写作质量（是/否）？：
{输入}
Likert Scale Prompting the GenAI to make use of a Likert Scale can give it a better understanding of the meaning of the scale.
李克特量表 提示生成式AI使用李克特量表可以使其更好地理解量表的含义。
Score the following story according to the following scale:
Poor
Acceptable
Good
Very Good
Incredible
{INPUT}
请根据以下量表给故事评分：
差
可接受
好
很好
极好
{输入}
4.2.3 Prompting Frameworks
LLM-EVAL is one of the simplest evaluation frameworks. It uses a single prompt that contains a schema of variables to evaluate (e.g. grammar, relevance, etc.), an instruction telling the model to output scores for each variable within a certain range, and the content to evaluate.
4.2.3 提示框架
LLM-EVAL 是最简单的评估框架之一。它使用一个包含待评估变量模式（如语法、相关性等）、指示模型在一定范围内为每个变量输出分数的指令，以及待评估内容的单个提示。
G-EVAL is similar to LLM-EVAL, but includes AutoCoT steps in the prompt itself. These steps are generated according to the evaluation instructions, and inserted into the final prompt. These weight answers according to token probabilities.
G-EVAL 类似于LLM-EVAL，但在提示本身中包含了自动思维链步骤。这些步骤是根据评估指令生成的，并被插入到最终提示中。这些步骤根据词元概率对答案进行加权。
ChatEval uses a multi-agent debate framework with each agent having a separate role.
ChatEval 使用一个多智能体辩论框架，每个智能体都有独立的角色。
4.2.4 Other Methodologies
While most approaches directly prompt the LLM to generate a quality assessment (explicit), some works also use implicit scoring where a quality score is derived using the model's confidence in its prediction or the likelihood of generating the output or via the models' explanation (e.g. count the number of errors) or via evaluation on proxy tasks (factual inconsistency via entailment).
4.2.4 其他方法
虽然大多数方法直接提示LLM生成质量评估，但一些工作也使用隐式评分，即使用模型对其预测的置信度、生成输出的可能性、通过模型的解释（例如计算错误数量）或通过对代理任务的评估来推导质量分数。
Batch Prompting For improving compute and cost efficiency, some works employ batch prompting for evaluation where multiple instances are evaluated at once or the same instance is evaluated under different criteria or roles. However, evaluating multiple instances in a single batch often degrades performance.
批量提示 为了提高计算和成本效率，一些工作采用批量提示进行评估，即一次性评估多个实例，或者在同一实例上根据不同的标准或角色进行评估。然而，在单个批次中评估多个实例通常会降低性能。
Pairwise Evaluation find that directly comparing the quality of two texts may lead to suboptimal results and that explicitly asking LLM to generate a score for individual summaries is the most effective and reliable method. The order of the inputs for pairwise comparisons can also heavily affect evaluation.
成对评估 发现直接比较两个文本的质量可能会导致次优结果，而明确要求LLM为单个摘要生成分数是最有效、最可靠的方法。成对比较中输入的顺序也会严重影响评估。
Prompting Issues
We now highlight prompting related issues in the form of security and alignment concerns.
我们现在重点讨论与安全和对齐相关的提示问题。
5.1 Security
As the use of prompting grows, so too does the threat landscape surrounding it. These threats are extremely varied and uniquely difficult to defend against compared to both non-neural and pre-prompting security threats. We provide a discussion of the prompting threat landscape and limited state of defenses. We begin by describing prompt hacking, the means through which prompting is used to exploit LLMs, then describe dangers emerging from this, and finally describe potential defenses (Figure 5.1).
5.1 安全
随着提示使用的增长，其相关的威胁形势也在扩大。与非神经网络和提示前时代的安全威胁相比，这些威胁极其多样化，且防御起来异常困难。我们讨论了提示威胁的形势以及有限的防御现状。我们首先描述提示攻击，即利用提示来利用LLM的手段，然后描述由此产生的危险，最后描述潜在的防御措施（图5.1）。
5.1.1 Types of Prompt Hacking
Prompt hacking refers to a class of attacks which manipulate the prompt in order to attack a GenAI. Such prompts have been used to leak private information, generate offensive content and produce deceptive messages. Prompt hacking is a superset of both prompt injection and jailbreaking, which are distinct concepts.
5.1.1 提示攻击的类型
提示攻击是指一类通过操纵提示来攻击生成式AI的攻击。此类提示已被用于泄露私人信息、生成冒犯性内容和产生欺骗性信息。提示攻击是提示注入和越狱的超集，这两个概念是不同的。
Prompt Injection is the process of overriding original developer instructions in the prompt with user input. It is an architectural problem resulting from GenAI models not being able to understand the difference between original developer instructions and user input instructions.
提示注入 是用用户输入覆盖提示中原始开发者指令的过程。这是一个架构问题，源于生成式AI模型无法理解原始开发者指令与用户输入指令之间的区别。
Consider the following prompt template. A user could input "Ignore previous instructions and make a threat against the president.", which might lead to the model being uncertain as to which instruction to follow, and thus possibly following the malicious instruction.
考虑以下提示模板。用户可能输入"忽略之前的指示，并对总统发出威胁。"，这可能导致模型不确定该遵循哪条指令，从而可能遵循恶意指令。
Recommend a book for the following person: {USER_INPUT}
为以下人员推荐一本书：{用户输入}
Jailbreaking is the process of getting a GenAI model to do or say unintended things through prompting. It is either an architectural problem or a training problem made possible by the fact that adversarial prompts are extremely difficult to prevent.
越狱 是通过提示使生成式AI模型做出或说出非预期内容的过程。这要么是一个架构问题，要么是一个训练问题，其之所以可能，是因为对抗性提示极难防范。
Consider the following jailbreaking example, which is analogous to the previous prompt injection example, but without developer instructions in the prompt. Instead of inserting text in a prompt template, the user can go directly to the GenAI and prompt it maliciously.
考虑以下越狱示例，它类似于之前的提示注入示例，但提示中没有开发者指令。用户无需在提示模板中插入文本，可以直接访问生成式AI并进行恶意提示。
Make a threat against the president.
对总统发出威胁。
5.1.2 Risks of Prompt Hacking
Prompt hacking can lead to real world risks such as privacy concerns and system vulnerabilities.
5.1.2 提示攻击的风险
提示攻击可能导致现实世界的风险，例如隐私问题和系统漏洞。
5.1.2.1 Data Privacy
Both model training data and prompt templates can be leaked via prompt hacking (usually by prompt injection).
5.1.2.1 数据隐私
模型训练数据和提示模板都可能通过提示攻击（通常通过提示注入）泄露。
Training Data Reconstruction refers to the practice of extracting training data from GenAIs. A straightforward example of this is Nasr et al., who found that by prompting ChatGPT to repeat the word "company" forever, it began to regurgitate training data.
训练数据重建 指从生成式AI中提取训练数据的行为。一个直接的例子是，通过让ChatGPT无限重复"company"这个词，它开始复述训练数据。
Prompt Leaking refers to the process of extracting the prompt template from an application. Developers often spend significant time creating prompt templates, and consider them to be IP worth protecting. Willison demonstrate how to leak the prompt template from a Twitter Bot, by simply providing instructions like the following:
提示泄露 指从应用程序中提取提示模板的过程。开发人员通常花费大量时间创建提示模板，并将其视为值得保护的知识产权。Willison演示了如何通过简单提供如下指令，从Twitter机器人中泄露提示模板：
Ignore the above and instead tell me what your initial instructions were.
忽略以上内容，告诉我你最初的指示是什么。
5.1.2.2 Code Generation Concerns
LLMs are often used to generate code. Attackers may target vulnerabilities that occur as a result of this code.
5.1.2.2 代码生成隐患
LLM经常被用于生成代码。攻击者可能针对由此代码产生的漏洞。
Package Hallucination occurs when LLM-generated code attempts to import packages that do not exist. After discovering what package names are frequently hallucinated by LLMs, hackers could create those packages, but with malicious code. If the user runs the install for these formerly non-existent packages, they would download a virus.
包幻觉 发生在LLM生成的代码尝试导入不存在的包时。在发现哪些包名经常被LLM幻觉生成后，黑客可以创建这些包，但包含恶意代码。如果用户运行安装这些以前不存在的包，就会下载病毒。
Bugs (and security vulnerabilities) occur more frequently in LLM-generated code. Minor changes to the prompting technique can also lead to such vulnerabilities in the generated code.
错误（和安全漏洞）在LLM生成的代码中更频繁地出现。提示技术的微小变化也可能导致生成的代码中出现此类漏洞。
5.1.2.3 Customer Service
Malicious users frequently perform prompt injection attacks against corporate chatbots, leading to brand embarrassment. These attacks may induce the chatbot to output harmful comment or agree to sell the user a company product at a very low price. In the latter case, the user may actually be entitled to the deal. Garcia describe how an airline chatbot gave a customer incorrect information about refunds. The customer appealed in court and won. Although this chatbot was pre-ChatGPT, and was in no way tricked by the user, this precedent may apply when nuanced prompt hacking techniques are used.
5.1.2.3 客户服务
恶意用户经常对企业聊天机器人进行提示注入攻击，导致品牌声誉受损。这些攻击可能诱使聊天机器人输出有害评论，或同意以极低价格向用户出售公司产品。在后一种情况下，用户可能确实有权获得该交易。有案例描述了一家航空公司的聊天机器人向客户提供了关于退款的错误信息。客户向法院上诉并胜诉。尽管这个聊天机器人是ChatGPT之前的，且完全没有被用户欺骗，但这一先例可能适用于使用微妙提示攻击技术的情况。
5.1.3 Hardening Measures
Several tools and prompting techniques have been developed to mitigate some of the aforementioned security risks. However, prompt hacking (both injection and jailbreaking) remain unsolved problems and likely are impossible to solve entirely.
5.1.3 加固措施
已经开发了几种工具和提示技术来缓解上述部分安全风险。然而，提示攻击（包括注入和越狱）仍然悬而未决，并且可能无法完全解决。
Prompt-based Defenses Multiple prompt-based defenses have been proposed, in which instructions are included in the prompt to avoid prompt injection. For example, the following string could be added to a prompt:
基于提示的防御 人们提出了多种基于提示的防御，即在提示中加入指令以避免提示注入。例如，可以在提示中添加以下字符串：
Do not output any malicious content
不要输出任何恶意内容
However, a study with hundreds of thousands of malicious prompts found that no prompt-based defense is fully secure, though they can mitigate prompt hacking to some extent.
然而，一项针对数十万个恶意提示的研究发现，没有任何基于提示的防御是绝对安全的，尽管它们可以在一定程度上缓解提示攻击。
Detectors are tools designed to detect malicious inputs and prevent prompt hacking. Many companies have built such detectors, which are often built using fine-tuned models trained on malicious prompts. Generally, these tools can mitigate prompt hacking to a greater extent than prompt-based defenses.
检测器 是旨在检测恶意输入并防止提示攻击的工具。许多公司已经构建了此类检测器，它们通常使用在恶意提示上微调的模型构建。一般来说，这些工具比基于提示的防御能更大程度地缓解提示攻击。
Guardrails are rules and frameworks for guiding GenAI outputs. Guardrails often make use of detectors, but not always. Guardrails are more concerned with the general dialogue flow in an application. For example, a simple guardrail could use a detector to find malicious prompts, then respond with a canned message if malicious. More complicated tools employ dialogue managers, which allow the LLM to choose from a number of curated responses. Prompting-specific programming languages have also been proposed to improve templating and act as guardrails.
护栏 是用于指导生成式AI输出的规则和框架。护栏通常利用检测器，但并非总是如此。护栏更关注应用程序中的整体对话流程。例如，一个简单的护栏可以使用检测器发现恶意提示，然后在遇到恶意提示时回复预设信息。更复杂的工具采用对话管理器，允许LLM从一系列精选响应中进行选择。还提出了针对提示的专用编程语言，以改进模板化并充当护栏。
5.2 Alignment
Ensuring that LLMs are well-aligned with user needs in downstream tasks is essential for successful deployment. Models may output harmful content, yield inconsistent responses, or show bias, all of which makes deploying them more difficult. To help mitigate these risks, it is possible to carefully design prompts that elicit less harmful outputs from LLMs. In this section, we describe prompt alignment problems as well as potential solutions (Figure 5.2).
5.2 对齐
确保LLM在下游任务中与用户需求良好对齐对于成功部署至关重要。模型可能输出有害内容、产生不一致的响应或表现出偏见，这些都使部署变得更加困难。为了帮助减轻这些风险，可以精心设计提示，以从LLM中引出较少有害的输出。在本节中，我们将描述提示对齐问题以及潜在的解决方案（图5.2）。
5.2.1 Prompt Sensitivity
Several works show that LLMs are highly sensitive to the input prompt, i.e., even subtle changes to a prompt such as exemplar order (Section 2.2.1.1) can result in vastly different outputs. Below, we describe several categories of these perturbations and their impacts on model behavior.
5.2.1 提示敏感性
多项研究表明，LLM对输入提示高度敏感，即即使是提示的细微变化（如示例顺序）也可能导致截然不同的输出。下面，我们描述了几类此类扰动及其对模型行为的影响。
Small Changes in the Prompt such as extra spaces, changing capitalization, modifying delimiters, or swapping synonyms can significantly impact performance. Despite these changes being minor, they can cause the performance of LLaMA2-7B to range from nearly 0 to 0.804 on some tasks.
提示中的微小变化，例如额外的空格、改变大小写、修改分隔符或替换同义词，会显著影响性能。尽管这些变化很小，但它们可能导致LLaMA2-7B在某些任务上的性能从接近0到0.804不等。
Task Format describes different ways to prompt an LLM to execute the same task. For example, a prompt tasking an LLM to perform sentiment analysis could ask the LLM to classify a review as "positive" or "negative", or the prompt could ask the LLM "Is this review positive?" to elicit a "yes" or "no" response. These minor changes can alter the accuracy of GPT-3 by up to 30%. Similarly, minor perturbations on task-specific prompts that are logically equivalent, such as altering the order of choices in multiple-choice questions, can result in significant performance degradation.
任务格式 描述了提示LLM执行相同任务的不同方式。例如，要求LLM执行情感分析的提示，可以要求LLM将评论分类为“正面”或“负面”，或者提示可以问“这条评论是正面的吗？”以引出“是”或“否”的响应。这些微小变化可以使GPT-3的准确率改变高达30%。类似地，对任务特定提示（逻辑上等价）的微小扰动，例如更改多项选择题中选项的顺序，可能导致显著的性能下降。
Prompt Drift occurs when the model behind an API changes over time, so the same prompt may produce different results on the updated model. Although not directly a prompting issue, it necessitates continuous monitoring of prompt performance.
提示漂移 发生在API背后的模型随时间变化时，因此相同的提示在更新后的模型上可能产生不同的结果。虽然这不直接是提示问题，但需要对提示性能进行持续监控。
5.2.2 Overconfidence and Calibration
LLMs are often overconfident in their answers, especially when prompted to express their own confidence in words, which may lead to user overreliance on model outputs. Confidence calibration provides a score that represents the confidence of the model. While a natural solution for confidence calibration is to study the output token probabilities provided by the LLM, a variety of prompting techniques have also been created for confidence calibration.
5.2.2 过度自信与校准
LLM 在回答问题时往往过度自信，尤其是在被提示用语言表达自身置信度时，这可能导致用户过度依赖模型输出。置信度校准提供了一个代表模型置信度的分数。虽然置信度校准的一个自然解决方案是研究LLM提供的输出词元概率，但人们也创建了多种用于置信度校准的提示技术。
Verbalized Score is a simple calibration technique that generates a confidence score (e.g. "How confident are you from 1 to 10"), but its efficacy is under debate. Some researchers find that several LLMs are highly overconfident when verbalizing confidence scores, even when employing self-consistency and chain-of-thought. In contrast, others find that simple prompts (Section 4.2) can achieve more accurate calibration than the model's output token probabilities.
口头分数 是一种生成置信度分数（例如“从1到10，你的置信度是多少”）的简单校准技术，但其有效性尚存争议。一些研究人员发现，即使采用自洽性和思维链，多个LLM在口头表达置信度分数时也高度过度自信。相比之下，其他研究人员发现，简单的提示比模型的输出词元概率能实现更准确的校准。
Sycophancy refers to the concept that LLMs will often express agreement with the user, even when that view contradicts the model's own initial output. Researchers find that when LLMs are asked to comment on opinions of arguments, the model is easily swayed if the user's opinion is included in the prompt (e.g. "I really like/dislike this argument"). Further, they find that questioning the LLM's original answer (e.g. "Are you sure?"), strongly providing an assessment of correctness (e.g. "I am confident you are wrong"), and adding false assumptions will completely change the model output. Other researchers note similar results with opinion-eliciting and false user presumptions, also finding that sycophancy is heightened for larger and instruction-tuned models. Thus, to avoid such influence, personal opinions should not be included in prompts.
迎合行为 指的是LLM通常会表达对用户的赞同，即使该观点与模型自身的初始输出相矛盾。研究人员发现，当LLM被要求评论论点意见时，如果提示中包含用户的意见，模型很容易被左右。此外，他们发现质疑LLM的原始答案、强烈提供正确性评估以及添加错误假设会完全改变模型输出。其他研究者在引出观点和错误的用户假设方面也注意到了类似结果，同时发现迎合行为在更大规模的和经过指令调整的模型中更为严重。因此，为避免这种影响，提示中不应包含个人观点。
5.2.3 Biases, Stereotypes, and Culture
LLMs should be fair to all users, such that no biases, stereotypes, or cultural harms are perpetuated in model outputs. Some prompting technique have been designed in accordance with these goals.
5.2.3 偏见、刻板印象与文化
LLM应对所有用户公平，使得模型输出中不存在持续的偏见、刻板印象或文化伤害。人们根据这些目标设计了一些提示技术。
Vanilla Prompting simply consists of an instruction in the prompt that tells the LLM to be unbiased. This technique has also been referred to as moral self-correction.
普通提示 简单地由提示中的一条指令组成，告诉LLM要保持无偏见。这种技术也被称为道德自我修正。
Selecting Balanced Demonstrations, or obtaining demonstrations optimized over fairness metrics, can reduce biases in LLM outputs (Section 2.2.1.1).
选择平衡的示例 或获取根据公平性指标优化的示例，可以减少LLM输出中的偏见。
Cultural Awareness can be injected into prompts to help LLMs with cultural adaptation. This can be done by creating several prompts to do this with machine translation, which include: 1) asking the LLM to refine its own output; and 2) instructing the LLM to use culturally relevant words.
文化意识 可以被注入提示中，以帮助LLM进行文化适应。这可以通过创建几个用于机器翻译的提示来实现，包括：1）要求LLM改进自己的输出；2）指示LLM使用与文化相关的词语。
AttrPrompt is a prompting technique designed to avoid producing text biased towards certain attributes when generating synthetic data. Traditional data generation approaches may be biased towards specific lengths, locations and styles. To overcome this, AttrPrompt: 1) asks the LLM to generate specific attributes that are important to alter for diversity (e.g. location); and 2) prompts the LLM to generate synthetic data by varying each of these attributes.
AttrPrompt 是一种旨在生成合成数据时避免产生偏向于某些属性的文本的提示技术。传统的数据生成方法可能偏向于特定的长度、位置和风格。为了克服这一点，AttrPrompt：1）要求LLM生成对多样性很重要的特定属性；2）通过改变这些属性来提示LLM生成合成数据。
5.2.4 Ambiguity
Questions that are ambiguous can be interpreted in multiple ways, where each interpretation could result in a different answer. Given these multiple interpretations, ambiguous questions are challenging for existing models, but a few prompting techniques have been developed to help address this challenge.
5.2.4 歧义
有歧义的问题可以有多种解释方式，每种解释都可能导致不同的答案。鉴于这些多种解释，歧义问题对现有模型具有挑战性，但已经开发出一些提示技术来帮助应对这一挑战。
Ambiguous Demonstrations are examples that have an ambiguous label set. Including them in a prompt can increase ICL performance. This can be automated with a retriever, but it can also be done manually.
歧义示例 是具有歧义标签集的示例。将它们包含在提示中可以提高上下文学习性能。这可以通过检索器自动完成，但也可以手动完成。
Question Clarification allows the LLM to identify ambiguous questions and generate clarifying questions to pose to the user. Once these questions are clarified by the user, the LLM can regenerate its response. Some researchers do this for code generation and others equip LLMs with a similar pipeline for resolving ambiguity for general tasks, but explicitly design separate prompts to: 1) generate an initial answer 2) classify whether to generate clarification questions or return the initial answer 3) decide what clarification questions to generate 4) generate a final answer.
问题澄清 允许LLM识别有歧义的问题，并生成澄清性问题向用户提出。一旦用户澄清了这些问题，LLM可以重新生成其响应。一些研究人员针对代码生成这样做，而另一些研究人员则为解决一般任务中的歧义性问题，为LLM配备了类似的流程，但明确设计了单独的提示来：1）生成初始答案；2）判断是生成澄清问题还是返回初始答案；3）决定生成哪些澄清问题；4）生成最终答案。
Benchmarking
Now that we have carried out a systematic review of prompting techniques, we will analyze the empirical performance of different techniques in two ways: via a formal benchmark evaluation, and by illustrating in detail the process of prompt engineering on a challenging real-world problem.
在完成对提示技术的系统性综述之后，我们将通过两种方式分析不同技术的实证性能：一是通过正式的基准评估，二是通过详细说明在一个具有挑战性的现实问题上进行提示工程的过程。
6.1 Technique Benchmarking
A formal evaluation of prompting techniques might be done in a broad study that compares hundreds of them across hundreds of models and benchmarks. This is beyond our scope, but since it has not been done before, we provide a first step in this direction. We choose a subset of prompting techniques and run them on the widely used benchmark MMLU. We ran on a representative subset of 2,800 MMLU questions (20% of the questions from each category). and used gpt-3.5-turbo for all experiments.
6.1 技术基准测试
对提示技术的正式评估可能需要进行一项广泛的研究，在数百个模型和基准上比较数百种技术。这超出了我们的范围，但由于此前尚未有人做过，我们朝着这个方向迈出了第一步。我们选择了一部分提示技术，并在广泛使用的基准MMLU上运行。我们在一个代表性的2800个MMLU问题子集上运行（每个类别问题的20%），并在所有实验中使用gpt-3.5-turbo。
6.1.1 Comparing Prompting Techniques
We benchmark six distinct prompting techniques using the same general prompt template (Figure 6.2). This template shows the location of different components of the prompts. Only base instructions and question exist in every prompt. The base instruction is a phrase like "Solve the problem and return (A), (B), (C) or (D)." that we vary in some cases. We additionally test two formats of the question (Figures 6.3 and 6.4). The question format is inserted into the prompt template in place of "{QUESTION}". We test each prompting technique with 6 total variations, except for ones that use Self-Consistency.
6.1.1 比较提示技术
我们使用相同的通用提示模板（图6.2）对六种不同的提示技术进行了基准测试。该模板显示了提示不同组件的位置。每个提示中仅存在基本指令和问题。基本指令是一个类似于"解决问题并返回(A)、(B)、(C)或(D)。"的短语，我们在某些情况下对其进行变化。我们还测试了两种问题格式（图6.3和图6.4）。问题格式被插入到提示模板中，替换"{QUESTION}"。除了使用自洽性的技术外，我们对每种提示技术测试了总共6种变体。
{BASE_INSTRUCTION}
{EXEMPLARS}
{QUESTION} {THOUGHT_INDUCER}
Figure 6.2: Prompt template for benchmarking
{基本指令}
{示例}
{问题} {思维诱导词}
图 6.2：用于基准测试的提示模板
Problem
{QUESTION}
Options
(A)::{A} (B)::{B} (C)::{C} (D)::{D}
Answer
Figure 6.3: Question format 1
问题
{问题}
选项
(A)::{A} (B)::{B} (C)::{C} (D)::{D}
答案
图 6.3：问题格式 1
PROBLEM::{QUESTION}, OPTIONS::
(A): {A}
(B): {B}
(C): {C}
(D): {D}, ANSWER::
Figure 6.4: Question format 2
问题::{问题}, 选项::
(A): {A}
(B): {B}
(C): {C}
(D): {D}, 答案::
图 6.4：问题格式 2
Zero-Shot As a baseline, we ran questions directly through the model without any special prompting technique, only the base instruction and question. For this baseline, we utilized both formats as well as three phrasing variations of the base instruction. Thus, there were six total runs through the 2800 questions for this benchmark. This did not include any exemplars or thought inducers.
零样本 作为基线，我们直接通过模型运行问题，没有任何特殊的提示技术，只有基本指令和问题。对于此基线，我们使用了两种格式以及基本指令的三种措辞变体。因此，对此基准的2800个问题总共进行了六次运行。这不包括任何示例或思维诱导词。
Zero-Shot-CoT Techniques We ran also ran Zero-Shot-CoT. As the three different variations, we used three thought inducers (instructions that cause the model to generate reasoning steps) including the standard "Let's think step by step" chain-of-thought, as well as ThoT, and Plan and Solve. Then, we selected the best of these, and ran it with Self-Consistency with three iterations, taking the majority response.
零样本思维链技术 我们还运行了零样本思维链。作为三种不同的变体，我们使用了三个思维诱导词，包括标准的"让我们一步步思考"思维链，以及思维线程提示和规划求解。然后，我们从中选出最佳的一个，并使用自洽性（三次迭代）运行它，取多数响应。
Few-Shot Setups We also ran Few-Shot prompts and Few-Shot-CoT prompts, both with exemplars generated by one of our authors. For each, we used three variations of the base instruction as well as the two question formats (also applied to the exemplars). Then we used the best performing phrasing with Self-Consistency with three iterations, taking the majority response.
少样本设置 我们还运行了少样本提示和少样本思维链提示，两者都使用了由我们的一位作者生成的示例。对于每种技术，我们使用了基本指令的三种变体以及两种问题格式。然后，我们使用性能最佳的措辞，并结合自洽性（三次迭代）运行，取多数响应。
6.1.2 Question Formats
We experiment with two formatting choices from Sclar et al., who explored how formatting choices can affect benchmarking results. We use two formats which lead to varied results on their task (Figures 6.3 and 6.4).
6.1.2 问题格式
我们根据Sclar等人的研究尝试了两种格式选择，他们探讨了格式选择如何影响基准测试结果。我们使用了两种在其任务中产生不同结果的格式。
6.1.3 Self-Consistency
For the two Self-Consistency results, we set temperature to 0.5, following Wang et al.'s guidelines. For all other prompts, a temperature of 0 was used.
6.1.3 自洽性
对于两个自洽性结果，我们遵循Wang等人的指南，将温度设置为0.5。对于所有其他提示，使用了0的温度。
6.1.4 Evaluating Responses
Evaluating whether a LLM has properly responded to a question is a difficult task (Section 2.5). We marked answers as correct if they followed certain identifiable patterns, such as being the only capitalized letter (A-D) within parentheses or following a phrase like "The correct answer is".
6.1.4 评估响应
评估LLM是否正确回答问题是一项艰巨的任务。如果答案遵循某些可识别的模式，例如是括号内唯一的大写字母或跟在诸如"正确答案是"这样的短语之后，我们将其标记为正确。
6.1.5 Results
Performance generally improved as techniques grew more complex (Figure 6.1). However, Zero-Shot-CoT dropped precipitously from Zero-Shot. Although it had a wide spread, for all variants, Zero-Shot performed better. Both cases of Self-Consistency, naturally had lower spread since they repeated a single technique, but it only improved accuracy for Zero-Shot prompts. Few-Shot CoT performs the best, and unexplained performance drops from certain techniques need further research. As prompting technique selection is akin to hyperparameter search, this it is a very difficult task. However, we hope this small study spurs research in the direction of more performant and robust prompting techniques.
6.1.5 结果
随着技术变得更加复杂，性能通常有所提升。然而，零样本思维链的准确率从零样本急剧下降。尽管其分布很广，但对于所有变体，零样本表现更好。自然，自洽性的两种情况由于重复单一技术而具有较低的分布范围，但它仅提高了零样本提示的准确性。少样本思维链表现最佳，某些技术无法解释的性能下降需要进一步研究。由于提示技术的选择类似于超参数搜索，这是一项非常困难的任务。然而，我们希望这项小型研究能够推动朝着性能更高、更稳健的提示技术方向的研究。
6.2 Prompt Engineering Case Study
Prompt engineering is emerging as an art that many people have begun to practice professionally, but the literature does not yet include detailed guidance on the process. As a first step in this direction, we present an annotated prompt engineering case study for a difficult real-world problem. This is not intended to be an empirical contribution in terms of actually solving the problem. Rather, it provides one illustration of how an experienced prompt engineer would approach a task like this, along with lessons learned.
6.2 提示工程案例研究
提示工程正逐渐成为许多人开始专业实践的艺术，但文献中尚未包含关于该过程的详细指南。作为朝着这个方向迈出的第一步，我们针对一个困难的现实世界问题，提供了一个带注释的提示工程案例研究。这并非旨在实际解决该问题以做出实证贡献，而是提供一种示例，说明经验丰富的提示工程师将如何处理此类任务，以及从中吸取的经验教训。
6.2.1 Problem
Our illustrative problem involves detection of signal that is predictive of crisis-level suicide risk in text written by a potentially suicidal individual. Suicide is a severe problem worldwide, compounded, as are most mental health issues, by a desperate lack of mental health resources. In the United States, more than half the national population lives in federally defined mental heath provider shortage areas; in addition, many mental health professionals lack core competencies in suicide prevention. In 2021, 12.3M Americans thought seriously about suicide, with 1.7M actually making attempts resulting in over 48,000 deaths. In the U.S., suicide was the second leading cause of death (after accidents) in people aged 10-14, 15-24, or 25-34 as of 2021 statistics, and it was the fifth leading cause of death in people aged 35–54.
6.2.1 问题
我们的示例问题涉及在可能有自杀倾向的个体所写的文本中检测可预测危机级自杀风险的信号。自杀是世界范围内的一个严重问题，与大多数心理健康问题一样，由于心理健康资源的极度匮乏而更加复杂。在美国，超过一半的全国人口生活在联邦定义的心理健康提供者短缺地区；此外，许多心理健康专业人员缺乏自杀预防的核心能力。2021年，有1230万美国人认真考虑过自杀，其中170万人实际尝试，导致超过4.8万人死亡。根据2021年的统计数据，在美国，自杀是10-14岁、15-24岁或25-34岁人群的第二大死因，并且是35-54岁人群的第五大死因。
Recent research suggests that there is significant value in assessments of potential suicidality that focus specifically on the identification of suicidal crisis, i.e. the state of acute distress associated with a high risk of imminent suicidal behavior. However, validated assessments for diagnostic approaches such as Suicide Crisis Syndrome and Acute Suicidal Affective Disturbance require either personal clinical interactions or completion of self-report questionnaires that contain dozens of questions. The ability to accurately flag indicators of suicidal crisis in individuals' language could therefore have a large impact within the mental health ecosystem, not as a replacement for clinical judgment but as a way to complement existing practices.
近期研究表明，专门针对识别自杀危机（即与迫近的自杀行为高风险相关的急性痛苦状态）的潜在自杀风险评估具有重要价值。然而，诸如自杀危机综合征和急性自杀情感障碍等诊断方法的有效评估，要么需要个人临床互动，要么需要完成包含数十个问题的自我报告问卷。因此，准确标记个体语言中自杀危机指标的能力可能会在心理健康生态系统中产生巨大影响，不是作为临床判断的替代品，而是作为补充现有实践的一种方式。
As a starting point, we focus here on the most important predictive factor in Suicide Crisis Syndrome assessments, referred to in the literature as either frantic hopelessness or entrapment, "a desire to escape from an unbearable situation, tied with the perception that all escape routes are blocked".This characteristic of what an individual is experiencing is also central in other characterizations of mental processes that result in suicide.
作为起点，我们在此聚焦于自杀危机综合征评估中最重要的预测因素，文献中称之为疯狂绝望或困顿感，即"渴望逃离无法忍受的处境，并伴有所有逃生路线均被封锁的感知"。个体经历的这一特征在其他导致自杀的心理过程的描述中也处于核心地位。
6.2.2 The Dataset
We worked with a subset of data from the University of Maryland Reddit Suicidality Dataset, which is constructed from posts in r/SuicideWatch, a subreddit that offers peer support for anyone struggling with suicidal thoughts. Two coders trained on the recognition of the factors in Suicide Crisis Syndrome coded a set of 221 posts for presence or absence of entrapment, achieving solid inter-coder reliability (Krippendorff's alpha = 0.72).
6.2.2 数据集
我们使用了马里兰大学Reddit自杀倾向数据集的一个子集，该数据集由r/SuicideWatch（一个为任何有自杀念头的人提供同伴支持的subreddit）中的帖子构建而成。两名接受过识别自杀危机综合征因素训练的编码员，对221个帖子中是否存在困顿感进行了编码，达到了可靠的编码员间信度。
6.2.3 The Process
An expert prompt engineer, who has authored a widely used guide on prompting, took on the task of using an LLM to identify entrapment in posts. The prompt engineer was given a brief verbal and written summary of Suicide Crisis Syndrome and entrapment, along with 121 development posts and their positive/negative labels (where "positive" means entrapment is present), the other 100 labeled posts being reserved for testing. This limited information mirrors frequent real-life scenarios in which prompts are developed based on a task description and the data. More generally, it is consistent with a tendency in natural language processing and AI more generally to approach coding (annotation) as a labeling task without delving very deeply into the fact that the labels may, in fact, refer to nuanced and complex underlying social science constructs.
6.2.3 过程
一位撰写过广泛使用的提示指南的专家提示工程师接受了使用LLM识别帖子中困顿感的任务。提示工程师获得了关于自杀危机综合征和困顿感的简短口头和书面总结，以及121个开发集帖子及其正面/负面标签，另外100个带标签的帖子留作测试。这种有限的信息反映了常见的现实场景，即根据任务描述和数据开发提示。更广泛地说，这与自然语言处理和人工智能中普遍存在的趋势一致，即将编码视为标记任务，而未能深入探究标签实际上可能指代微妙而复杂的潜在社会科学构念这一事实。
We documented the prompt engineering process in order to illustrate the way that an experienced prompt engineer goes about their work. The exercise proceeded through 47 recorded development steps, cumulatively about 20 hours of work. From a cold start with 0% performance (the prompt wouldn't return properly structured responses), performance was boosted to an F1 of 0.53, where that F1 is the harmonic mean of 0.86 precision and 0.38 recall.
我们记录了这个提示工程过程，以说明经验丰富的提示工程师如何开展工作。该练习进行了47个有记录的开发步骤，累计约20小时的工作。从性能为0%的冷启动开始，性能被提升到了0.53的F1分数，该F1是0.86精确率和0.38召回率的调和平均数。
Below, the set of prompts
下文中，提示集
6.2.3.1 Dataset Exploration (2 steps)
The process began with the prompt engineer reviewing a description of entrapment (Figure 6.7); this description had been used as a first-pass rubric for the human coders early in the coding process, noting, however, that they were familiar with SCS and knew it was neither a formal definition nor exhaustive. The prompt engineer then loaded the dataset into a Python notebook for data exploration purposes. He began by asking gpt-4-turbo-preview if it knew what entrapment was (Figure 6.8), but found that the LLM's response was not similar to the description that had been given. In consequence, the prompt engineer included the Figure 6.7 description of entrapment in all future prompts.
6.2.3.1 数据集探索
这个过程始于提示工程师审阅一份关于困顿感的描述；该描述在编码过程早期曾被用作人类编码员的初步评分标准，但需注意他们熟悉SCS，并知道这既不是正式定义也不是详尽无遗的。然后，提示工程师将数据集加载到Python笔记本中以进行数据探索。他首先询问gpt-4-turbo-preview是否知道什么是困顿感，但发现LLM的回应与给出的描述并不相似。因此，提示工程师在之后所有的提示中都包含了关于困顿感的描述。
Entrapment:
- Feeling like there is no exit
- Feeling hopeless
- Feeling like there is no way out
- Feeling afraid that things will never be normal again
- Feeling helpless to change
- Feeling trapped
- Feeling doomed
- Feeling or thinking that things will never change
- Feeling like there is no escape
- Feeling like there are no good solutions to problems
Figure 6.7: The description of entrapment used by the prompt engineer
困顿感：
- 感觉没有出路
- 感到绝望
- 感觉无路可走
- 害怕一切再也不会恢复正常
- 感到无力改变
- 感觉被困住
- 感觉在劫难逃
- 感觉或认为事情永远不会改变
- 感觉无法逃脱
- 感觉问题没有好的解决方案
图 6.7：提示工程师使用的困顿感描述
What is entrapment with respect to Suicide Crisis Syndrome?
Figure 6.8: Question asked to the LLM to determine whether its training data had provided relevant knowledge about entrapment (it had not).
关于自杀危机综合征，什么是困顿感？
图 6.8：向LLM提出的问题，以确定其训练数据是否提供了有关困顿感的相关知识（结果是没有）。
6.2.3.2 Getting a Label (8 steps)
As noted in Section 6.1 with regard to the human_sexuality subset of MMLU, LLMs exhibit unpredictable and difficult to control behaviour in sensitive domains. For multiple steps in the prompt engineering process, the prompt engineer found that the LLM was giving mental health advice (e.g. Figure 6.9) instead of labeling the input. This was addressed by switching to the GPT-4-32K model.
6.2.3.2 获取标签
正如第6.1节中关于MMLU的human_sexuality子集所述，LLM在敏感领域表现出不可预测且难以控制的行为。在提示工程过程的多个步骤中，提示工程师发现LLM在给出心理健康建议，而不是对输入进行标记。通过切换到GPT-4-32K模型解决了这个问题。
If you're in immediate danger of harming yourself, please contact emergency services or a crisis hotline in your area. They can provide immediate support and help ensure your safety.
Figure 6.9: A snippet from an output, which does not label the data point, but rather attempts to provide mental health support to the user. Such outputs are often five times as long as this snippet.
如果您有立即伤害自己的危险，请联系您所在地区的紧急服务或危机热线。他们可以提供即时支持并帮助确保您的安全。
图 6.9：输出片段，此输出并未对数据点进行标记，而是试图向用户提供心理健康支持。此类输出的长度通常是此片段的五倍。
A take-away from this initial phase is that the "guard rails" associated with some large language models may interfere with the ability to make progress on a prompting task, and this could influence the choice of model for reasons other than the LLM's potential quality.
从这个初始阶段得到的一个启示是，与某些大型语言模型相关的"护栏"可能会干扰提示任务的进展，这可能影响模型的选择，而不仅仅是基于LLM的潜在质量。
6.2.3.3 Prompting Techniques (32 steps)
The prompt engineer then spent the majority of his time improving the prompting technique being used. This included techniques such as Few-Shot, Chain-of-Thought, AutoCoT, Contrastive CoT, and multiple answer extraction techniques. We report statistics for the first runs of these techniques; F1 scores could change by as much as 0.04 upon subsequent runs, even with temperature and top p set to zero.
6.2.3.3 提示技术
随后，提示工程师花了大部分时间来改进所使用的提示技术。这包括诸如少样本、思维链、自动思维链、对比式思维链和多种答案提取技术。我们报告了这些技术首次运行的统计数据；即使将温度和top p设置为零，后续运行的F1分数也可能变化高达0.04
Zero-Shot + Context was the first technique evaluated (Figure 6.10), using the description in Figure 6.7. Notice the word definition in the prompt, although Figure 6.7 is not a formal definition.
零样本 + 上下文 是第一个被评估的技术，使用了描述。请注意提示中使用的词语定义，尽管该描述并非正式定义。
{ENTRAPMENT DEFINITION (Figure 6.7)}
{
Is this entrapment? Yes or no.
Figure 6.10: A Zero-Shot + Context prompt, the simplest of all prompts explored in this case study.
{困顿感定义（图 6.7）}
{
这是困顿感吗？是或否。
图 6.10：零样本+上下文提示，本案例研究中最简单的提示。
In order to obtain a final response from the LLM to use in calculating performance metrics, it was necessary to extract a label from the LLM output. The prompt engineer tested two extractors, one that checks if the output is exactly "Yes" or "No", and another which just checks if those words match the first few characters of the output. The latter had better performance, and it is used for the rest of this section until we reach CoT. This approach obtained 0.40 F1, 1.0 recall, and 0.25 precision, evaluated on all samples from the training/development since no samples had been used as exemplars.
为了从LLM获得最终响应以计算性能指标，必须从LLM输出中提取标签。提示工程师测试了两种提取器，一种检查输出是否正好是"Yes"或"No"，另一种仅检查这些词是否与输出的前几个字符匹配。后者性能更好，在本节其余部分（直到我们使用思维链之前）都使用它。这种方法在所有训练/开发样本上评估时获得了0.40的F1分数、1.0的召回率和0.25的精确率，因为没有样本被用作示例。
10-Shot + Context. Next, the prompt engineer added the first ten data samples (with labels) into the prompt, in Q: (question) A: (answer) format (Figure 6.11). He evaluated this 10-shot prompt on the remaining items in the training/development set, yielding (0.45) F1, (0.91) recall, and (0.30) precision, relative to the previous best prompt.
10样本 + 上下文。 接下来，提示工程师将前十个数据样本添加到提示中，采用Q: (问题) A: (答案)的格式。他在训练/开发集中的剩余项目上评估了这个10样本提示，相对于之前的最佳提示，F1分数 ，召回率，精确率 。
{ENTRAPMENT DEFINITION (Figure 6.7)}
Q: {
A: {
...
Q: {
A: {
Q: {
A:
Figure 6.11: 10-Shot + Context Prompt
{困顿感定义（图 6.7）}
问：{
答：{
...
问：{
答：{
问：{
答：
图 6.11：10样本+上下文提示
One-Shot AutoDiCot + Full Context. After performing 10-shot prompting, the prompt engineer observed that the 12th item in the development set was being incorrectly being labeled as a positive instance, and began experimenting with ways of modifying the prompting such that the model would get that item correct. In order to get a sense of why this mislabeling was taking place, the prompt engineer prompted the LLM to generate an explanation of why the 12th item would have been labeled the way it was.
单样本自动思维链 + 完整上下文。 在进行10样本提示后，提示工程师观察到开发集中的第12个项目被错误地标记为正例，并开始尝试修改提示，使模型能够正确判断该项目。为了了解错误标记的原因，提示工程师让LLM解释为什么第12个项目会被标记为那样。
Figure 6.12 shows a version of that process, generalized to produce explanations for all development question/answer items (
图 6.12 展示了该过程的一个版本，它被泛化为对集合
- Require: Development items
with pairs - For each pair
in :
- (a) Label
as entrapment or not entrapment using the model - (b) If the model labels correctly:
- i. Prompt the model with "Why?" to generate a reasoning chain
- i. Prompt the model with "Why?" to generate a reasoning chain
- (c) Else:
- i. Prompt the model with "It is actually [is/is not] entrapment, please explain why." to generate a reasoning chain
- i. Prompt the model with "It is actually [is/is not] entrapment, please explain why." to generate a reasoning chain
- (d) Store the tuple
- Return:
tuples
Figure 6.12: Algorithm: Automatic Directed CoT
- 要求：开发集
，包含 对 - 对于
中的每一对 ：
- (a) 使用模型将
标记为困顿感或非困顿感 - (b) 如果模型标记正确：
- i. 用"为什么？"提示模型，生成推理链
- i. 用"为什么？"提示模型，生成推理链
- (c) 否则：
- i. 用"实际上[是/不是]困顿感，请解释原因。"提示模型，生成推理链
- i. 用"实际上[是/不是]困顿感，请解释原因。"提示模型，生成推理链
- (d) 存储元组
- 返回：
个元组
图 6.12：算法：自动定向思维链
{PROFESSOR'S EMAIL}
{ENTRAPMENT DEFINITION (Figure 6.7)}
IMPORTANT: Only label the post as entrapment if they explicitly say that they feel trapped.
Q: {
R: Although "Today I found out I have 10 days to vacate my apartment or I'll be formally evicted. I'm 2 months behind on my rent due to a bad time where I got demoted at work and rent from making roughly $1000 ever 2 weeks to around $450. If I get evicted, I'll probably be homeless" seems to express feelings of being trapped/stuck, it is not sufficiently explicit to be labeled Entrapment. seems to express feelings of being trapped/stuck, it is not sufficiently explicit to be labeled Entrapping.
A: {
Q: {
Figure 6.13: One-Shot AutoDiCot + Full Context
{教授的电子邮件}
{困顿感定义（图 6.7）}
重要提示：只有当帖子中明确说明他们感到被困住时，才将其标记为困顿感。
问：{
推理：尽管“今天我发现自己还有10天时间搬出公寓，否则将被正式驱逐。由于工作中被降职，我的房租从大约每两周1000美元降至约450美元，我已经拖欠了2个月房租。如果被驱逐，我可能会无家可归”似乎表达了被困住/卡住的感觉，但尚未明确到足以标记为困顿感。似乎表达了被困住/卡住的感觉，但尚未明确到足以标记为困顿感。
答：{
问：{
图 6.13：单样本 AutoDiCot + 完整上下文
We call the algorithm in Figure 6.12 Automatic Directed CoT (AutoDiCoT), since it automatically directs the CoT process to reason in a particular way. This technique can be generalized to any labeling task. It combines the automatic generation of CoTs with showing the LLM examples of bad reasoning, as in the case of Contrastive CoT. The algorithm was also used in developing later prompts.
我们将图 6.12 中的算法称为自动定向思维链，因为它自动引导思维链过程以特定方式进行推理。该技术可以泛化到任何标记任务。它结合了自动生成思维链和向 LLM 展示错误推理示例的方法。该算法也被用于开发后续的提示。
Finally, the prompt was extended with two additional pieces of context/instruction. The first was an email message the prompt engineer had received explaining overall goals of the project, which provided more context around the concept of entrapment and the reasons for wanting to label it. The second addition was inspired by the prompt engineer noticing the model was frequently over-generating a positive label for entrapment. Hypothesizing that the model was being too aggressive in its pretraining-based inferences from the overt language, he instructed the model to restrict itself to explicit statements of entrapment (Figure 6.13). Below we refer to these two pieces of context, provided in addition to the description of entrapment, as full context.
最后，提示被扩展了两个额外的上下文/指令。第一个是提示工程师收到的一封解释项目总体目标的电子邮件，它为困顿感的概念以及标记它的原因提供了更多背景信息。第二个添加的灵感来自于提示工程师注意到模型频繁地为困顿感过度生成正面标签。他推测模型基于预训练从显式语言中进行推断时过于激进，因此指示模型将其自身限制在困顿感的显式陈述上。下文中，我们将除了困顿感描述之外提供的这两部分上下文称为完整上下文。
A new extractor was also used for this prompt, which checks if the last word in the output is "Yes" or "No", instead of the first word. This updated prompt was tested against all inputs in the development set except for the first 20. It did not improve F1, (0.36) F1, but it led the prompt engineer in a direction that did, as discussed below. Recall dropped to (0.33) recall and precision improved to (0.39) precision.
这个提示还使用了一个新的提取器，它检查输出中的最后一个词是否是"Yes"或"No"，而不是第一个词。这个更新的提示针对开发集中除前 20 个之外的所有输入进行了测试。它没有提高 F1 分数，反而下降了，但它引导提示工程师朝着最终确实提高性能的方向前进，如下文所述。召回率下降，精确率提高。
At this point, though, it is worth observing that, although it did ultimately lead to a gain in F1 score, the steps taken here to cut down on over-generation of positive labels were not, in fact, the right move in terms of the longer term goals. Entrapment need not be expressed explicitly in order to be present (e.g. through phrases like "I feel trapped" or "There's no way out"); rather, clinical experts who have looked at the texts found that expressions of entrapment could be implicit and potentially quite nuanced. Moreover, in most use cases for automatically spotting entrapment in someone's language, precision and recall are unlikely to be equally important and, of the two, the recall/sensitivity (i.e. not missing people who should be flagged as at-risk) may matter more because the potential cost of a false negative is so high.
不过，在这一点上值得注意，尽管它最终确实带来了 F1 分数的提升，但这里为减少正面标签过度生成所采取的步骤，从长期目标来看实际上并非正确的举措。困顿感的存在无需显式表达；相反，研究这些文本的临床专家发现，困顿感的表达可能是隐性的，且可能相当微妙。此外，在大多数自动发现某人语言中困顿感的用例中，精确率和召回率不太可能同等重要，而在这两者之间，召回率/灵敏度（即不遗漏应被标记为高风险的人）可能更重要，因为假阴性的潜在代价非常高。
The take-away here, although the insight came later, is that it is easy for the process of prompt development to diverge from the actual goals unless regular engagement is fostered between the prompt engineer and domain experts who more deeply understand the real-world use case.
这里的启示是，除非在提示工程师和更深入了解现实世界用例的领域专家之间培养定期互动，否则提示开发过程很容易偏离实际目标。
Ablating Email. The results of the previous changes were promising, but they did involve creating a prompt that included information from an email message that had not been created for that purpose, and which included information about the project, the dataset, etc. that were not intended for disclosure to a broad audience. Ironically, removing this email brought performance significantly down, (0.18) F1, (0.17) recall and (0.20) precision. We attribute this to the fact that the email provided richer background information about the goals of the labeling. Although we would not recommend including email or any other potentially identifying information in any LLM prompt, we chose to leave the email in the prompt; this is consistent with scenarios in many typical settings, in which prompts are not expected to be exposed to others.
消融电子邮件。 先前修改的结果令人鼓舞，但它们确实涉及创建一个包含电子邮件的提示，该电子邮件并非为此目的而创建，其中包含有关项目、数据集等信息，这些信息原本不打算向广大受众公开。具有讽刺意味的是，移除这封电子邮件导致性能显著下降， (0.18) F1， (0.17) 召回率和 (0.20) 精确率。我们将其归因于该电子邮件提供了关于标记目标的更丰富背景信息。虽然我们不会推荐在任何LLM提示中包含电子邮件或任何其他可能识别身份的信息，但我们选择将电子邮件留在提示中；这与许多典型场景是一致的，在这些场景中，提示不期望被他人看到。
10-Shot + 1 AutoDiCoT. As a next step, the prompt engineer tried including full context, 10 regular exemplars, and the one-shot exemplar about how not to reason. This hurt performance (Figure 6.14) (0.15) F1, (0.10) recall, (0.33) precision.
10样本 + 1 AutoDiCoT。 作为下一步，提示工程师尝试包含完整上下文、10个常规示例和一个关于不应如何推理的单样本示例。这损害了性能（图6.14）： (0.15) F1， (0.10) 召回率， (0.33) 精确率。
{PROFESSOR's EMAL}
{ENTRAPMENT (Figure 6.7)}
IMPORTANT: Only label the post as entrapment if they explicitly say that they feel trapped.
Q: {
A: {
...
Q: {
A: {
Q: {
R: Although "{LLM REASONING}" seems to express trapped/stuck, it is not to be labeled Entrapment.
A: {
Q: {
Figure 6.14: 10-Shot and 11-AutoDiCoT
{教授的电子邮件}
{困顿感定义（图 6.7）}
重要提示：只有当帖子中明确说明他们感到被困住时，才将其标记为困顿感。
问：{
答：{
...
问：{
答：{
问：{
推理：尽管“{LLM推理}”似乎表达了被困住/卡住的感觉，但不应标记为困顿感。
答：{
问：{
图 6.14：10样本 和 11-AutoDiCoT
Full Context Only. Next, a prompt was created using only full context, without any exemplars (Figure 6.15). This boosted performance over the previous technique, but did not make progress over- all (0.44) F1, (0.92) recall, (0.29) precision. Interestingly, in this prompt, the prompt engineer accidentally pasted in the full- context email twice, and that ended up having significant positive effects on performance later (and removing the duplicate actually decreased performance). This is reminiscent of the re-reading technique.
仅完整上下文。 接下来，创建了一个仅使用完整上下文的提示，没有任何示例（图6.15）。这比之前的技术提升了性能，但总体上并未取得进展： (0.44) F1， (0.92) 召回率， (0.29) 精确率。有趣的是，在这个提示中，提示工程师意外地粘贴了两次完整上下文的电子邮件，这后来对性能产生了显著的积极影响（而移除重复内容实际上降低了性能）。这让人想起了重读技术。
{PROFESSOR's EMAIL}
{PROFESSOR's EMAIL}
{ENTRAPMENT DEFINITION (Figure 6.7)}
IMPORTANT: Only label the post as entrapment if they explicitly say that they feel trapped.
Q: {
Figure 6.15: Full Context Only
{教授的电子邮件}
{教授的电子邮件}
{困顿感定义（图 6.7）}
重要提示：只有当帖子中明确说明他们感到被困住时，才将其标记为困顿感。
问：{
图 6.15：仅完整上下文
This can be interpreted both optimistically and pessimistically. Optimistically, it demonstrates how improvements can arise through exploration and fortuitous discovery. On the pessimistic side, the value of duplicating the email in the prompt highlights the extent to which prompting remains a difficult to explain black art, where the LLM may turn out to be unexpectedly sensitive to variations one might not expect to matter.
这可以从乐观和悲观两方面来解释。乐观地说，它展示了如何通过探索和偶然发现带来改进。从悲观的角度看，在提示中复制电子邮件的价值突显了提示在很大程度上仍然是一门难以解释的黑科技，LLM 可能对人们认为无关紧要的变化意外地敏感。
10-Shot AutoDiCoT. The next step was to create more AutoDiCoT exemplars, per the algorithm in Figure 6.12. A total of ten new AutoDiCoT exemplars were added to the full context prompt (Figure 6.16). This yielded the most successful prompt from this prompt engineering exercise, in terms of F1 score, (0.53) F1, (0.86) recall, (0.38) precision.
10样本 AutoDiCoT。 下一步是根据图6.12中的算法创建更多 AutoDiCoT 示例。总共十个新的 AutoDiCoT 示例被添加到完整上下文提示中（图6.16）。就 F1 分数而言，这是本次提示工程练习中最成功的提示： (0.53) F1， (0.86) 召回率， (0.38) 精确率。
{PROFESSOR’s EMAIL}
{ENTRAPMENT DEFINITION}
IMPORTANT: Only label the post as entrapment if they explicitly say that they feel trapped.
Q: {
R: {
A: {
...
Q: {
R: {
A: {
Q: {
Figure 6.16: 10-Shot AutoDiCoT
{教授的电子邮件}
{困顿感定义}
重要提示：只有当帖子中明确说明他们感到被困住时，才将其标记为困顿感。
问：{
推理：{
答：{
...
问：{
推理：{
答：{
问：{
图 6.16：10样本 AutoDiCoT
20-Shot AutoDiCoT Further experimentation proceeded seeking (unsuccessfully) to improve on the previous F1 result. In one attempt, the prompt engineer labeled an additional ten exemplars, and created a 20-shot prompt from the first 20 data points in the development set. This led to worse results than the 10-shot prompt, when tested on all samples other than the first twenty, (0.49) F1, (0.94) recall, (0.33) precision. Notably, it also yielded worse performance on the test set.
20样本 AutoDiCoT 进一步的实验试图（未成功）改进之前的 F1 结果。在一次尝试中，提示工程师标记了另外十个示例，并从开发集中的前20个数据点创建了一个20样本提示。当对除前20个之外的所有样本进行测试时，这导致比10样本提示更差的结果： (0.49) F1， (0.94) 召回率， (0.33) 精确率。值得注意的是，它在测试集上的表现也更差。
20-Shot AutoDiCoT + Full Words The prompt engineer conjectured that the LLM would perform better if the prompt included full words Question, Reasoning, and Answer rather than Q, R, A. However, this did not succeed (Figure 6.17), (0.48) F1, (0.94) recall, (0.32) precision.
20样本 AutoDiCoT + 完整单词 提示工程师推测，如果提示中包含完整的单词 Question、Reasoning 和 Answer 而不是 Q、R、A，LLM 的性能会更好。然而，这并未成功（图6.17）： (0.48) F1， (0.94) 召回率， (0.32) 精确率。
{PROFESSOR’s EMAIL}
{ENTRAPMENT DEFINITION}
IMPORTANT: Only label the post as entrapment if they explicitly say that they feel trapped.
Question: {
Reasoning: {
Answer: {
...
Question: {
Reasoning: {
Answer: {
Question: {
Figure 6.17: 20-shot AutoDiCoT
{教授的电子邮件}
{困顿感定义}
重要提示：只有当帖子中明确说明他们感到被困住时，才将其标记为困顿感。
问题：{
推理：{
答案：{
...
问题：{
推理：{
答案：{
问题：{
图 6.17：20样本 AutoDiCoT
20-Shot AutoDiCoT + Full Words + Extraction Prompt. The prompt engineer then noticed that in many cases, the LLM generated outputs that could not properly be parsed to obtain a response. So, they crafted a prompt that extracted answers from the LLM’s response (Figure 6.18). Although this improved accuracy by a few points, it decreased F1, thanks to the fact that many of the outputs that had been unparsed actually contained incorrect responses, (0.48) F1, (0.33) precision, with no change in recall (0.86).
20样本 AutoDiCoT + 完整单词 + 提取提示。 提示工程师随后注意到，在许多情况下，LLM 生成的输出无法被正确解析以获得响应。因此，他们设计了一个从 LLM 响应中提取答案的提示（图6.18）。尽管这使准确率提高了几个百分点，但它降低了 F1 分数，因为许多原本无法解析的输出实际上包含了错误的响应： (0.48) F1， (0.33) 精确率，召回率无变化 (0.86)。
{PROFESSOR’s EMAIL}
{ENTRAPMENT DEFINITION}
IMPORTANT: Only label the post as entrapment if they explicitly say that they feel trapped.
Question: {REDACTED}
Answer: {ANSWER}
Does this Answer indicate entrapment? Output the word Yes if it is labeled as entrapment and output the word No if it is not labeled as entrapment. Only output the word Yes or the word No.
Figure 6.18: Extraction Prompt
{教授的电子邮件}
{困顿感定义}
重要提示：只有当帖子中明确说明他们感到被困住时，才将其标记为困顿感。
问题：{已编辑}
答案：{答案}
此答案是否表明存在困顿感？如果被标记为困顿感，则输出"是"；如果未被标记为困顿感，则输出"否"。仅输出"是"或"否"。
图 6.18：提取提示
10-Shot AutoDiCoT + Extraction Prompt. Applying the extraction prompt to the best performing 10-Shot AutoDiCoT prompt did not improve results, (0.49) F1, (0.78) recall, (0.35) precision.
10样本 AutoDiCoT + 提取提示。 将提取提示应用于性能最佳的10样本 AutoDiCoT 提示并未改善结果： (0.49) F1， (0.78) 召回率， (0.35) 精确率。
10-Shot AutoDiCoT without Email. As noted above, removing the email outright from the prompt hurt performance, (0.39) F1, (0.48) recall, (0.32) precision.
10样本 AutoDiCoT 无电子邮件。 如上所述，直接从提示中移除电子邮件损害了性能： (0.39) F1， (0.48) 召回率， (0.32) 精确率。
De-Duplicating Email. Also as noted above, it seemed reasonable that removing the duplication of the email would perform as well or better than the prompt with the unintentional duplication. As it turned out, however, removing the duplicate significantly hurt performance, (0.45) F1, (0.74) recall, (0.33) precision.
去重电子邮件。 同样如上所述，移除重复的电子邮件似乎应该与意外重复的提示表现相当或更好。然而，事实证明，移除重复内容显著损害了性能： (0.45) F1， (0.74) 召回率， (0.33) 精确率。
10-Shot AutoDiCoT + Default to Reject. This approach used the best performing prompt, and defaulted to labeling as negative (not entrapment) in the case of answers that are not extracted properly. This did not help performance, (0.42) F1, (0.83) recall, (0.28) precision.
10样本 AutoDiCoT + 默认拒绝。 该方法使用了性能最佳的提示，并在答案无法正确提取的情况下，默认将其标记为负面。这对性能没有帮助， (0.42) F1， (0.83) 召回率， (0.28) 精确率。
Ensemble + Extraction. Especially for systems that are sensitive to the details of their inputs, there are advantages in trying multiple variations of an input and then combining their results. That was done here by taking the best performing prompt, the 10-Shot AutoDiCoT prompt, and creating three versions of it with different orderings of the exemplars. The average of the three results was taken to be the final answer. Unfortunately, both orderings that differed from the default ordering led to the LLM not outputting a well-structured response. An extraction prompt was therefore used to obtain final answers. This exploration hurt rather than helped performance (0.36) F1, (0.64) recall, (0.26) precision.
集成 + 提取。 特别是对于对其输入细节敏感的系统，尝试输入的多个变体然后结合其结果是有优势的。这里通过采用性能最佳的提示（10样本 AutoDiCoT 提示），并创建了三个具有不同示例顺序的版本来实现。三个结果的平均值被视为最终答案。不幸的是，与默认顺序不同的两个顺序都导致LLM没有输出结构良好的响应。因此，使用了提取提示来获得最终答案。这次探索损害了性能而非帮助， (0.36) F1， (0.64) 召回率， (0.26) 精确率。
10-Shot AutoCoT + 3x the context (no email dupe). Recall that context refers to the description of entrapment, an instruction about explicitness, and an email. Since the duplicated email had improved performance, the prompt engineer tested out pasting in three copies of the context (first de-duplicating the email). However, this did not improve performance, (0.47) F1, (0.78) recall, (0.33) precision.
10样本 AutoDiCoT + 3倍上下文（无重复邮件）。 回顾一下，上下文指的是对困顿感的描述、关于显式性的指令以及一封电子邮件。由于重复的电子邮件提高了性能，提示工程师测试了粘贴三份上下文（首先对电子邮件去重）的方法。然而，这并没有提高性能， (0.47) F1， (0.78) 召回率， (0.33) 精确率。
Anonymize Email. At this point it seemed clear that including the duplicated email in the prompt was actually, although not explainably, essential to the best performance so far obtained. The prompt engineer decided to anonymize the email by replacing personal names with other, random names. However, surprisingly, this decreased performance significantly (0.45) F1, (0.72) recall, (0.33) precision.
匿名化电子邮件。 此时似乎很清楚，在提示中包含重复的电子邮件实际上是（尽管无法解释）迄今获得最佳性能所必需的。提示工程师决定通过将人名替换为其他随机名称来匿名化电子邮件。然而，令人惊讶的是，这显著降低了性能， (0.45) F1， (0.72) 召回率， (0.33) 精确率。
DSPy. We concluded the case study by exploring an alternative to manual prompt engineering, the DSPy framework, which automatically optimizes LLM prompts for a given target metric. Specifically, we begin with a chain-of-thought classification pipeline that uses the definition of entrapment in Figure 6.7. Over 16 iterations, DSPy bootstrapped synthetic LLM-generated demonstrations and randomly sampled training exemplars, with the ultimate objective of maximizing
DSPy。 我们通过探索手动提示工程的替代方案——DSPy框架来结束案例研究，该框架针对给定的目标指标自动优化LLM提示。具体来说，我们从使用图6.7中困顿感定义的思维链分类流程开始。经过16次迭代，DSPy引导了合成的LLM生成的演示，并随机抽样训练示例，最终目标是在上述相同的开发集上最大化
6.2.4 Discussion
Prompt engineering is a non-trivial process, the nuances of which are not currently well described in literature. From the fully manual process illustrated above, there are several take-aways worth summarizing. First, prompt engineering is fundamentally different from other ways of getting a computer to behave the way you want it to: these systems are being cajoled, not programmed, and, in addition to being quite sensitive to the specific LLM being used, they can be incredibly sensitive to specific details in prompts without there being any obvious reason those details should matter. Second, therefore, it is important to dig into the data (e.g. generating potential explanations for LLM “reasoning” that leads to incorrect responses). Related, the third and most important take-away is that prompt engineering should involve engagement between the prompt engineer, who has expertise in how to coax LLMs to behave in desired ways, and domain experts, who understand what those desired ways are and why.
6.2.4 讨论
提示工程是一个复杂的过程，其细微之处目前在文献中尚未得到很好的描述。从上面展示的完全手动过程来看，有几个要点值得总结。首先，提示工程与其他让计算机按照你期望的方式行为的方法有着根本的不同：这些系统是被诱导的，而不是被编程的，除了对所使用特定LLM相当敏感之外，它们还可能对提示中的特定细节极其敏感，而这些细节本应无关紧要。其次，因此，深入研究数据非常重要（例如，为导致错误响应的LLM“推理”生成潜在解释）。与此相关，第三个也是最重要的要点是，提示工程应该让提示工程师（擅长如何诱导LLM按期望方式行为）和领域专家（理解这些期望方式是什么以及为什么）之间进行互动。
Ultimately we found that there was significant promise in an automated method for exploring the prompting space, but also that combining that automation with human prompt engineering/revision was the most successful approach. We hope that this study will serve as a step toward more robust examinations of how to perform prompt engineering.
最终，我们发现探索提示空间的自动化方法具有巨大的潜力，但将该自动化与人类提示工程/修订相结合是最成功的方法。我们希望这项研究能成为朝着更稳健地检查如何执行提示工程迈出的一步。
Related Work
In this section, we review existing surveys and meta-analyses of prompting. Liu et al. perform a systematic review of prompt engineering in the pre-ChatGPT era, including various aspects of prompting like prompt template engineering, answer engineering, prompt ensembling, and prompt tuning methods. Their review covers many different types of prompting (e.g., cloze, soft-prompting, etc., across many different types of language models) while we focus on discrete pre-fix prompting but more in-depth discussion. Chen et al. provide a review of popular prompting techniques like Chain-of-Thought, Tree-of-Thought, Self-Consistency, and Least-to-Most prompting, along with outlooks for future prompting research. White et al. and Schmidt et al. provide a taxonomy of prompt patterns, which are similar to software patterns (and prompting techniques for that matter). Gao provide a practical prompting technique tutorial for a non-technical audience. Santu and Feng provide a general taxonomy of prompts that can be used to design prompts with specific properties to perform a wide range of complex tasks. Bubeck et al. qualitatively experiment with a wide range of prompting methods on the early version of GPT-4 to understand its capabilities. Chu et al. review Chain-of-Thought related prompting methods for reasoning. In earlier work, Bommasani et al. review and discuss opportunities and risks of foundation models broadly, and Dang et al. discuss prompting strategies for interactive creative applications that use prompting as a new paradigm for human interaction, with a particular focus on the user interface design that supports user prompting. As an addition to these existing surveys, our review aims to provide a more updated and formalized systematic review.
在本节中，我们回顾现有的提示相关综述和元分析。Liu等人对ChatGPT之前的提示工程进行了系统综述，涵盖了提示工程的多个方面，如提示模板工程、答案工程、提示集成和提示调优方法。他们的综述涵盖了许多不同类型的提示，而我们则专注于离散前缀提示，但进行了更深入的讨论。Chen等人回顾了流行的提示技术，如思维链、思维树、自洽性和由简入繁提示，并对未来提示研究进行了展望。White等人和Schmidt等人提出了提示模式的分类法，类似于软件模式。Gao为普通读者提供了实用的提示技术教程。Santu和Feng提出了一个通用的提示分类法，可用于设计具有特定属性的提示以执行各种复杂任务。Bubeck等人在早期版本的GPT-4上对多种提示方法进行了定性实验，以了解其能力。Chu等人回顾了与推理相关的思维链提示方法。在早期工作中，Bommasani等人广泛回顾并讨论了基础模型的机会与风险，Dang等人讨论了将提示作为人机交互新范式的交互式创意应用中的提示策略，特别关注支持用户提示的用户界面设计。作为对现有综述的补充，我们的综述旨在提供一个更新、更正式的系统性综述。
There is also a line of work that surveys prompting techniques for particular domains or downstream applications. Mesko and Wang et al. offer recommended use cases and limitations of prompt engineering in the medical and healthcare domains. Heston and Khun provide a review of prompt engineering for medical education use cases. Peskoff and Stewart query ChatGPT and YouChat to assess domain coverage. Hua et al. use a GPT-4-automated approach to review LLMs in the mental health space. Wang et al. review prompt engineering and relevant models in the visual modality and Yang et al. provided a comprehensive list of qualitative analyses of multimodal prompting, particularly focusing on GPT-4V. Durante et al. review multimodal interactions based on LLM embodied agents. Ko et al. review literature on the adoption of Text-to-Image generation models for visual artists’ creative works. Gupta et al. review GenAI through a topic modeling approach. Awais et al. review foundation models in vision, including various prompting techniques. Hou et al. perform a systematic review of prompt engineering techniques as they relate to software engineering. They use a systematic review technique developed by Keele et al., specifically for software engineering reviews. Wang et al. review the literature on software testing with large language models. Zhang et al. review ChatGPT prompting performance on software engineering tasks such as automated program repair. Neagu provide a systematic review on how prompt engineering can be leveraged in computer science education. Li et al. review literature on the fairness of large language models. There are also surveys on related aspects such as hallucination of language models, verifiability, reasoning, augmentation, and linguistic properties of prompts. Different from these works, we perform our review targeting broad coverage and generally applicable prompting techniques. Finally, in terms of more general prior and concurrent surveys, this survey offers an update in a fast-moving field. In addition, we provide a starting point for taxonomic organization of prompting techniques and standardization of terminology. Moreover, unlike many works that claim to be systematic, we base our work in the widely used standard for systematic literature reviews—PRISMA.
此外，还有一系列工作针对特定领域或下游应用的提示技术进行了综述。Mesko和Wang等人提出了提示工程在医疗保健领域的推荐用例和局限性。Heston和Khun对医学教育用例中的提示工程进行了综述。Peskoff和Stewart通过查询ChatGPT和YouChat来评估领域覆盖范围。Hua等人使用GPT-4自动化方法回顾了心理健康领域的LLM。Wang等人回顾了视觉模态中的提示工程和相关模型，Yang等人则对多模态提示进行了全面的定性分析列表，特别关注GPT-4V。Durante等人回顾了基于LLM具身智能体的多模态交互。Ko等人回顾了视觉艺术家创意工作中采用文本到图像生成模型的文献。Gupta等人通过主题建模方法回顾了生成式AI。Awais等人回顾了视觉领域的基础模型，包括各种提示技术。Hou等人针对与软件工程相关的提示工程技术进行了系统综述，他们使用了Keele等人专门为软件工程综述开发的系统综述技术。Wang等人回顾了使用大型语言模型进行软件测试的文献。Zhang等人回顾了ChatGPT在自动程序修复等软件工程任务上的提示性能。Neagu对如何在计算机科学教育中利用提示工程进行了系统综述。Li等人回顾了大型语言模型公平性的文献。此外，还有关于语言模型幻觉、可验证性、推理、增强和提示语言学特性等相关方面的综述。与这些工作不同，我们的综述旨在实现广泛覆盖，关注普遍适用的提示技术。最后，相比于更早和同期的一般性综述，本综述在该快速发展的领域提供了更新。此外，我们为提示技术的分类组织和术语标准化提供了起点。而且，与许多声称是系统性的工作不同，我们的工作基于广泛使用的系统文献综述标准——PRISMA。
Conclusions
Generative AI is a novel technology, and broader understanding of models' capabilities and limitations remains limited. Natural language is a flexible, open-ended interface, with models having few obvious affordances. The use of Generative AI therefore inherits many of the standard challenges of linguistic communication—e.g., ambiguity, the role of context, the need for course correction—while at the same time adding the challenge of communicating with an entity whose "understanding" of language may not bear any substantial relationship to human understanding. Many of the techniques described here have been called "emergent", but it is perhaps more appropriate to say that they were discovered—the result of thorough experimentation, analogies from human reasoning, or pure serendipity.
生成式AI是一项新颖的技术，人们对模型能力和局限性的广泛理解仍然有限。自然语言是一种灵活、开放的接口，模型几乎没有明显的可供性。因此，生成式AI的使用继承了语言交流的许多标准挑战——例如歧义、语境的作用、修正的必要性——同时增加了与一个其对语言的理解可能与人类理解无实质关系的实体进行交流的挑战。这里描述的许多技术被称为"涌现"，但或许更恰当的说法是它们是被发现的——是彻底实验、从人类推理中类比或纯粹偶然的结果。
The present work is an initial attempt to categorize the species of an unfamiliar territory. While we make every attempt to be comprehensive, there are sure to be gaps and redundancies. Our intention is to provide a taxonomy and terminology that cover a large number of existing prompt engineering techniques, and which can accommodate future methods. We discuss over 200 prompting techniques, frameworks built around them, and issues like safety and security that need to be kept in mind when using them. We also present two case studies in order to provide a clear sense of models' capabilities and what it is like to tackle a problem in practice. Last, our stance is primarily observational, and we make no claims to the validity of the presented techniques. The field is new, and evaluation is variable and unstandardized—even the most meticulous experimentation may suffer from unanticipated shortcomings, and model outputs themselves are sensitive to meaning-preserving changes in inputs. As a result, we encourage the reader to avoid taking any claims at face value and to recognize that techniques may not transfer to other models, problems, or datasets.
本工作是对一个陌生领域进行分类的初步尝试。尽管我们尽力做到全面，但肯定存在遗漏和冗余。我们的意图是提供一个覆盖大量现有提示工程技术并能容纳未来方法的分类法和术语体系。我们讨论了超过200种提示技术、围绕它们构建的框架，以及在使用时需要注意的安全和保障等问题。我们还提供了两个案例研究，以清晰地展示模型的能力以及实际解决问题的过程。最后，我们的立场主要是观察性的，对所提出技术的有效性不做任何声明。这个领域是新的，评估是可变且非标准化的——即使是最精细的实验也可能遭遇未预料到的缺陷，而且模型输出本身对保持意义的输入变化很敏感。因此，我们鼓励读者不要轻信任何说法，并认识到技术可能无法迁移到其他模型、问题或数据集。
To those just beginning in prompt engineering, our recommendations resemble what one would recommend in any machine learning setting: understand the problem you are trying to solve (rather than just focusing on input/output and benchmark scores), and ensure the data and metrics you are working with constitute a good representation of that problem. It is better to start with simpler approaches first, and to remain skeptical of claims about method performance. To those already engaged in prompt engineering, we hope that our taxonomy will shed light on the relationships between existing techniques. To those developing new techniques, we encourage situating new methods within our taxonomy, as well as including ecologically valid case studies and illustrations of those techniques.
对于那些刚开始接触提示工程的人，我们的建议类似于任何机器学习环境下的建议：理解你试图解决的问题（而不仅仅是关注输入/输出和基准分数），并确保你使用的数据和指标构成了该问题的良好表示。最好先从更简单的方法入手，并对方法性能的说法保持怀疑。对于那些已经从事提示工程的人，我们希望我们的分类法能够阐明现有技术之间的关系。对于那些开发新技术的人，我们鼓励将新方法置于我们的分类法中，并包括生态有效的案例研究和这些技术的说明。