This paper delves into the pivotal role of prompt engineering in unleashing
the capabilities of Large Language Models (LLMs). Prompt engineering is the
process of structuring input text for LLMs and is a technique integral to
optimizing the efficacy of LLMs. This survey elucidates foundational principles
of prompt engineering, such as role-prompting, one-shot, and few-shot
prompting, as well as more advanced methodologies such as the chain-of-thought
and tree-of-thoughts prompting. The paper sheds light on how external
assistance in the form of plugins can assist in this task, and reduce machine
hallucination by retrieving external knowledge. We subsequently delineate
prospective directions in prompt engineering research, emphasizing the need for
a deeper understanding of structures and the role of agents in Artificial
Intelligence-Generated Content (AIGC) tools. We discuss how to assess the
efficacy of prompt methods from different perspectives and using different
methods. Finally, we gather information about the application of prompt
engineering in such fields as education and programming, showing its
transformative potential. This comprehensive survey aims to serve as a friendly
guide for anyone venturing through the big world of LLMs and prompt
engineering.

该综述研究了快速建模在发挥大型语言模型的能力方面的关键作用，探讨了角色激励、单次指示、观点链、观点树等快速建模的基本原则和高级方法，并阐述了外部插件如何协助此任务并减少机器幻觉，强调了深入了解结构和代理角色在人工智能生成内容工具中的重要性，最后讨论了在教育和编程等领域中应用快速建模的实践和潜力。

大型语言模型中开发提示工程的潜力释放：综合回顾

Unleashing the potential of prompt engineering in Large Language Models:  a comprehensive review

The recent progress in large language models (LLMs), especially the invention
of chain-of-thoughts (CoT) prompting, makes it possible to solve reasoning
problems. However, even the strongest LLMs are still struggling with more
complicated problems that require non-linear thinking and multi-step reasoning.
In this work, we explore whether LLMs have the ability to recognize their own
errors, without resorting to external resources. In particular, we investigate
whether they can be used to identify individual errors within a step-by-step
reasoning. To this end, we propose a zero-shot verification scheme to recognize
such errors. We then use this verification scheme to improve question-answering
performance, by using it to perform weighted voting on different generated
answers. We test the method on three math datasets-GSM8K, MathQA, and MATH-and
find that it successfully recognizes errors and, in turn, increases final
predictive performance.

最近大语言模型（LLM），特别是链式思维（CoT）提示的发明，使得解决推理问题成为可能。本文探讨 LLMs 是否具备识别自身错误的能力，无需外部资源。具体而言，我们研究它们是否能够用于识别逐步推理中的个别错误。为此，我们提出了一种零 - shot 验证方案来识别这些错误。然后，我们使用这个验证方案来提高问题回答的性能，通过对不同生成答案进行加权投票。我们在三个数学数据集 - GSM8K，MathQA 和 MATH 上测试了该方法，发现它成功地识别错误，并进而提高了最终的预测性能。

SelfCheck: 使用 LLMs 检查自己的逐步推理的零 - shot 方法

SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step  Reasoning

Recently, there has been significant progress in teaching language models to
perform step-by-step reasoning to solve complex numerical reasoning tasks.
Chain-of-thoughts prompting (CoT) is by far the state-of-art method for these
tasks. CoT uses language models to perform both reasoning and computation in
the multi-step `thought' process. To disentangle computation from reasoning, we
propose `Program of Thoughts' (PoT), which uses language models (mainly Codex)
to express the reasoning process as a program. The computation is relegated to
an external computer, which executes the generated programs to derive the
answer. We evaluate PoT on five math word problem datasets (GSM, AQuA, SVAMP,
TabMWP, MultiArith) and three financial-QA datasets (FinQA, ConvFinQA, TATQA)
for both few-shot and zero-shot setups. Under both few-shot and zero-shot
settings, PoT can show an average performance gain over CoT by around 12\%
across all the evaluated datasets. By combining PoT with self-consistency
decoding, we can achieve SoTA performance on all math problem datasets and
near-SoTA performance on financial datasets. All of our data and code are
released in
Github\footnote{https://github.com/wenhuchen/Program-of-Thoughts}.

用自然语言生成理解模型将数值推理任务中的计算和推理过程分离成程序，使得计算过程集中于电脑中，从而提高了数学问题和金融问题的解决性能。