生物医学的大型语言模型:基础,机遇,挑战和最佳实践
Satya S Sahoo1, Joseph M Plasek2, Hua Xu3
1Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44122, United States.
概括
生成型大语言模型 (LLM) 为生物医学信息学提供了强大的自然语言处理 (NLP) 功能. 本指南概述了在医疗保健研究和临床应用中有效整合LLM的机会,挑战和最佳实践.
科学领域:
- 生物医学信息学 生物医学信息学
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 生成型大语言模型 (LLM),一种基于变压器的神经网络,在各种自然语言处理 (NLP) 任务中表现出色.
- 少数射击和零射击学习使LLM能够在各种NLP应用中实现最先进的性能.
- 美国医学信息学协会 (AMIA) 的NLP工作组旨在弥合LLM和生物医学信息学社区之间的差距.
研究的目的:
- 描述将LLMs纳入生物医学NLP的机会和挑战.
- 为在下游NLP应用中利用LLM提供最佳实践.
- 为使临床医生和研究人员对NLP不熟悉更容易获得LLM.
主要方法:
- 专注于三个核心的NLP任务类别:理解,推断和生成.
- 审查提示调整,指令微调和LLM评估指标.
- 讨论新兴技术,如增强提示,指令提示调整,以及从人类反 (RLHF) 中进行强化学习.
主要成果:
- 在生物医学NLP应用中,LLM为推进NLP应用提供了重大机会.
- 关键的挑战包括迷信/幻觉,毒性和数据集污染.
- 新兴的能力和技术,如思维链提示,提供潜在的解决方案.
结论:
- 有效整合LLMs需要解决生物医学NLP当前的挑战.
- 最佳实践和新兴技术可以提高LLM在医疗保健中的实用性.
- 持续的研究和社区参与对于最大限度地发挥生物医学信息学LLM潜力至关重要.
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