术语
领域(数学分析)
计算机科学
领域特定语言
数据科学
软件工程
数学分析
哲学
语言学
数学
作者
Jamil Zaghir,Marco Naguib,Mina Bjelogrlic,Aurélie Névéol,Xavier Tannier,Christian Lovis
摘要
Prompt engineering, focusing on crafting effective prompts to large language models (LLMs), has garnered attention for its capabilities at harnessing the potential of LLMs. This is even more crucial in the medical domain due to its specialized terminology and language technicity. Clinical natural language processing applications must navigate complex language and ensure privacy compliance. Prompt engineering offers a novel approach by designing tailored prompts to guide models in exploiting clinically relevant information from complex medical texts. Despite its promise, the efficacy of prompt engineering in the medical domain remains to be fully explored.
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