用大模型自动评估医学问答，省去医生人工审核时间。
Towards Leveraging Large Language Models for Automated Medical Q&A Evaluation
- 用患者数据生成问题，测试大模型能否替代人工评分
- 初步结果表明大模型可可靠复现人类评估效果
- 适合需要快速筛选医学问答质量的研究者使用
本文探索利用大语言模型（LLMs）自动化评估医学问答系统中的回答质量，这是自然语言处理中的一项关键任务。传统上，医疗专业人士的人工评估是确保回答质量的必要环节，但耗时且成本高昂。本研究通过使用来自真实患者数据的问题，检验了大模型是否能可靠地复现人类评估结果，从而为医疗专家节省宝贵时间。研究结果显示具有前景，但针对更具体或复杂问题的评估仍需进一步研究。
原文摘要 · Abstract (English)
This paper explores the potential of using Large Language Models (LLMs) to automate the evaluation of responses in medical Question and Answer (Q\&A) systems, a crucial form of Natural Language Processing. Traditionally, human evaluation has been indispensable for assessing the quality of these responses. However, manual evaluation by medical professionals is time-consuming and costly. Our study examines whether LLMs can reliably replicate human evaluations by using questions derived from patient data, thereby saving valuable time for medical experts. While the findings suggest promising results, further research is needed to address more specific or complex questions that were beyond the scope of this initial investigation.
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