在医学对话中探索ChatGPT的潜力 摘要:关于与人类偏好一致性的研究
Yong Liu1, Shenggen Ju2, Junfeng Wang1
1Department of Computer Science, Sichuan University, No. 24, South Section 1, 1st Ring Road, Chendu, 610065, Sichuan, China.
BMC medical informatics and decision making
|March 15, 2024
概括
像GPT-3.5这样的大型语言模型 (LLM) 对自动化医疗对话总结有希望,在人类评估中表现优于传统模型. 然而,目前的ROUGE和BERTScore等自动化指标并不能完全捕捉到LLM的业绩,因此需要新的评估标准.
科学领域:
- 自然语言处理自然语言处理.
- 医疗保健中的人工智能
背景情况:
- 在COVID-19大流行期间,远程医疗的采用加速,增加了对远程医疗保健服务的需求.
- 远程医疗的激增凸显了需要有效的自动化医疗对话总结来管理漫长的咨询.
- 大型语言模型 (LLM) 在自然语言处理 (NLP) 研究中呈现出一个新的前沿,在医疗保健中具有潜在的应用.
研究的目的:
- 在医学对话总结中评估LLM,特别是GPT-3.5的表现.
- 为了比较基于LLM的总结与传统方法,如基于BERT的总结 (BERTSUM) 和BART.
- 评估自动化指标 (ROUGE,BERTScore) 和医疗专家手动评估的有效性.
主要方法:
- 精心调整的预先训练的语言模型 (BERTSUM,BART) 用于医学对话总结.
- 作为第二种方法,采用了GPT-3.5与语境间学习 (ICL).
- 通过自动化指标 (ROUGE,BERTScore) 和医学专家的手动评估来评估表现.
主要成果:
- 在自动化和手动评估中,BERTSUM的表现不佳.
- 巴特实现了比ChatGPT更高的ROUGE和BERTScore值,但在人工专家审查中显示出局限性.
- 尽管自动分数较低,但ChatGPT (GPT-3.5) 摘要因可理解性和质量而受到医学专家的青.
结论:
- GPT-3.5显示了医学对话总结的巨大潜力,提供了可理解和受欢迎的成果.
- 自动评估指标 (ROUGE,BERTScore) 不足以准确评估LLM生成的摘要.
- 需要进行进一步的研究,以制定更合适的评估标准,以在医疗环境中对LLM成果进行评估.
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