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

Social determinants of health (SDOH) documented in the electronic health
record through unstructured text are increasingly being studied to understand
how SDOH impacts patient health outcomes. In this work, we utilize the Social
History Annotation Corpus (SHAC), a multi-institutional corpus of de-identified
social history sections annotated for SDOH, including substance use,
employment, and living status information. We explore the automatic extraction
of SDOH information with SHAC in both standoff and inline annotation formats
using GPT-4 in a one-shot prompting setting. We compare GPT-4 extraction
performance with a high-performing supervised approach and perform thorough
error analyses. Our prompt-based GPT-4 method achieved an overall 0.652 F1 on
the SHAC test set, similar to the 7th best-performing system among all teams in
the n2c2 challenge with SHAC.

本文使用 SHAC 数据集中的社交史部分，采用 GPT-4 的一次性提示設置探索 SDOH 信息的自动提取，其中包括物質使用，就业和生活状况信息，和采用高性能监督方法将 GPT-4 提取性能与其进行比较，并进行彻底的错误分析。GPT-4 的提示方法在 SHAC 测试中获得了总体 0.652 的 F1，类似于 n2c2 挑战中最好的 7 个系统之一