通过快速调整,使用大型语言模型自动总结医生与患者接触对话
Mengxian Lyu1, Cheng Peng1, Xiaohan Li1
1Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.
概括
生成型的大型语言模型 (LLM) 可以有效地通过提示调整来总结医生与患者的对话. 这种高效的方法提供了一个低成本的解决方案,用于临床环境中的自动文本总结.
科学领域:
- 人工智能的人工智能
- 临床信息学 临床信息学
- 自然语言处理自然语言处理.
背景情况:
- 自动文本总结 (ATS) 对于无的临床护理至关重要.
- 生成型大语言模型 (LLM) 为总结复杂的医学对话提供了潜力.
研究的目的:
- 开发和评估一个快速调整的方法,用生成的LLMs来总结医生和患者之间的对话.
- 评估临床LLMGatorTronGPT的性能,与基于T5的微调模型对比.
主要方法:
- 开发了提示调整算法,以指导临床文本总结的生成LLM.
- 检查了提示调整策略,软提示大小和少数镜头学习能力.
- 使用MTS-DIALOG临床基准数据集进行评估.
主要成果:
- 在所有评估指标上,GatorTron GPT-20B 模型表现出卓越的性能.
- 提示调整被证明是计算效率高,因为LLM参数保持不变.
- 这种方法有效地总结了临床文本,展示了生成性临床LLMs的实用性.
结论:
- 生成性临床LLM,当由提示调整指导时,对于自动文本总结是有效的.
- 这种方法提供了一个低成本和有效的解决方案来总结医生与患者之间的对话.
- 该研究强调了快速调整的潜力,以促进临床信息学的发展.
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