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Updated: Jul 17, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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选择性UMLS知识输入用于回答生物医学问题.

Hyeryun Park1,2, Jiye Son1,2, Jeongwon Min1,2

  • 1Interdisciplinary Program for Bioengineering, Seoul National University Graduate School, Seoul, Republic of Korea.

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|August 30, 2023
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概括

本研究介绍了一种有效的方法,用于将生物医学知识集成到使用适配器的AI语言模型中. 在特定的知识图分区上预训练适配器可以提高生物医学问答性能,降低计算成本.

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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:

  • 知识密集型问答是生物医学中一个关键的人工智能应用.
  • 领域专业知识至关重要,需要有效的知识输入到语言模型中.
  • 目前传输大型知识图的方法在计算上昂贵.

研究的目的:

  • 提出一种有效的方法,将生物医学知识注入预训练的语言模型中,用于问答.
  • 调查使用所有知识图语义的必要性.
  • 探索分区知识图的策略,以实现高效的预训练.

主要方法:

  • 利用适配器将统一医疗语言系统 (UMLS) 知识注入预训练的语言模型.
  • 调查分区知识图的策略,包括丢弃或合并语义组.
  • 评估三个生物医学问题答案微调数据集的性能.

主要成果:

  • 在语义分区的知识图组上进行预训练的适配器在评估指标,参数数量和时间方面表现出更好的效率.
  • 对于较小的数据集,抛弃具有较少概念的知识组更有效.
  • 合并这些组对于较大的数据集来说更有利.
  • 适配器方法表明对特定组的配方不敏感,但有轻微的指标改进.

结论:

  • 基于适配器的知识输入提供了一种有效的方法来增强生物医学问答模型.
  • 战略分区和知识图组件的选择优化了预训练的效率.
  • 该方法在不同的数据集大小和知识图结构中是稳健的.