LEAP:LLM指令-示例适应性提示框架用于生物医学关系提取
Huixue Zhou1, Mingchen Li2, Yongkang Xiao1
1Institute for Health Informatics, University of Minnesota, Minneapolis, MN 55455, United States.
概括
通过使用自适应提示,LEAP框架增强了用于生物医学关系提取的大型语言模型 (LLM). 这种方法比标准调提高了性能,特别是在复杂的数据提取任务中.
科学领域:
- 生物医学信息学是生物医学信息学.
- 自然语言处理自然语言处理.
- 人工智能的人工智能
背景情况:
- 大型语言模型 (LLM) 对生物医学关系提取有希望.
- 在LLMs当前的演示方法需要优化复杂的生物医学任务.
- 需要适应性调整策略来提高LLM在专业领域的表现.
研究的目的:
- 研究生物医学关系提取中的大型语言模型 (LLM) 的自适应调整方法.
- 引入和评估LLM指令-示例自适应提示 (LEAP) 框架.
- 评估适应性任务描述和示例对LLM绩效的影响.
主要方法:
- 在生物医学数据任务中分析了LLM的演示组件 (任务描述,示例).
- 开发和实施LEAP框架,使用三个适应性调整方法:指令,示例和指令-示例适应性调整.
- 在使用Llama2和MedLLaMA等LLM对DDI,ChemProt和BioRED数据集进行LEAP评估.
主要成果:
- 指令 + 选项 + 例子促使F1得分明显提高,与零射击LLMs的标准方法相比.
- 在所有测试模型中,LEAP框架,特别是适应性提示的例子,在所有测试模型中都超过了传统的指令调整.
- MedLLAMA_13B在ChemProt上获得了95.13的F1分,在DDI和BioRED数据集上表现出强的表现.
结论:
- LEAP框架提供了一种有效的策略,以加强生物医学关系提取的LLM培训.
- 这种方法倾向于动态,上下文丰富的提示,而不是广泛的微调.
- 在复杂的数据提取场景中,LEAP为提高LLM能力提供了一个有希望的方向.
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