语言模型及其在生物医学中的可解释性:一个范围审查
Daoming Lyu1,2, Xingbo Wang1,2, Yong Chen3
1Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, New York, NY, USA.
iScience
|March 18, 2024
概括
大型语言模型 (LLM) 为生物医学研究提供了强大的AI能力. 本文审查了它们的使用,挑战和未来的研究方向,重点是医疗保健应用中的可解释性.
科学领域:
- 生物医学研究的研究.
- 人工智能的人工智能是人工智能.
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 的进步正在改变人工智能 (AI).
- 可以将LLM重新用于各种下游任务,承诺从生物医学数据中进行可概括的表示.
- 它们的新兴特性需要更深入地了解它们在医疗保健中的能力和局限性.
研究的目的:
- 综合审查LLM在生物医学中的应用.
- 确定新兴的研究领域和挑战,特别是关于模型解释性的问题.
- 引导研究人员和从业人员在生物医学环境中有效和道德地利用LLMs.
主要方法:
- 在生物医学领域使用语言模型的研究的系统文献综述.
- 分析研究趋势,应用和报告的挑战.
- 确定与模型解释性和未来研究机会相关的关键主题.
主要成果:
- 在医疗保健和生物医学研究中,LLM显示出对推进人工智能的重大承诺.
- 目前对LLM行为,故障模式和功能的理解是有限的.
- 解释性仍然是生物医学LLM的关键技术和分析挑战.
结论:
- 对生物医学LLM的全面理解对于其负责任的开发和部署至关重要.
- 未来的研究应该专注于提高LLM的解释性,并解决它们的新兴特性.
- 本综述为研究人员提供了一份路线图,以导航医疗保健中AI不断变化的景观.
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