Prompt engineering paradigms for medical applications: scoping review and recommendations for better practices
arxiv(2024)
摘要
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
non-prompt-related baselines. We provide tables and figures summarizing
existing work, and reporting recommendations to guide future research
contributions.
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