Abstract
Prompt engineering is crucial for harnessing the potential of large language models (LLMs), especially in the medical domain where specialized terminology and phrasing is used. However, the efficacy of prompt engineering in the medical domain remains to be explored. In this work, 114 recent studies (2022-2024) applying prompt engineering in medicine, covering prompt learning (PL), prompt tuning (PT), and prompt design (PD) are reviewed. PD is the most prevalent (78 articles). In 12 papers, PD, PL, and PT terms were used interchangeably. ChatGPT is the most commonly used LLM, with seven papers using it for processing sensitive clinical data. Chain-of-Thought emerges as the most common prompt engineering technique. While PL and PT articles typically provide a baseline for evaluating prompt-based approaches, 64% of PD studies lack non-prompt-related baselines. We provide tables and figures summarizing existing work, and reporting recommendations to guide future research contributions.
Abstract (translated)
提示工程对于挖掘大型语言模型的（LLM）潜力非常重要，尤其是在医疗领域，其中使用了专门的术语和措辞。然而，在医疗领域中提示工程的有效性仍需探讨。在这篇工作中，我们回顾了114篇最近发表的（2022-2024）医学领域的提示工程论文，包括提示学习（PL）、提示调整（PT）和提示设计（PD）。其中，提示设计最为普遍（78篇）。在12篇论文中，PD、PL和PT术语被交替使用。ChatGPT是最常用的LLM，有7篇论文用于处理敏感的临床数据。 Chain-of-Thought成为最常用的提示工程技术。虽然PL和PT文章通常为基于提示的方法提供基准，但64%的PD研究缺乏非提示相关的基准。我们提供了总结现有工作的表格和图表，并报告了指导未来研究贡献的建议。
URL
https://arxiv.org/abs/2405.01249
