赛拉克特:零射击生物医学文档级关系提取与协同RAG和CoT.
Xin Dong1, Di Zhao1,2, Jiana Meng1
1School of Computer Science and Engineering, Dalian Minzu University, Liaoning 116600, China.
Bioinformatics (Oxford, England)
|June 27, 2025
概括
SyRACT框架通过对大型语言模型 (LLM) 重构任务,使用PubMed数据,并采用思维链推理来增强生物医学关系提取,从而显著提高了准确性.
科学领域:
- 生物医学信息学 生物医学信息学
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 为生物医学文档级关系提取 (BioDocRE) 提供了新的可能性.
- 然而,在BioDocRE任务中,LLM与幻觉,有限的推理和缺乏可解释性作斗争.
研究的目的:
- 引入SyRACT框架,用于高精度的BioDocRE.
- 在基于LLM的BioDocRE中解决幻觉,推理和可解释性的挑战.
主要方法:
- 重构了BioDocRE作为一个问答任务,以更好地调整LLM.
- 利用PubMed衍生的外部数据库提供可靠的上下文信息,减少幻觉.
- 为BioDocRE开发了一个特定的思维链 (CoT) 策略,以改善推理和解释性.
主要成果:
- 在三个数据集 (CDR,GDA,ADE) 中,SyRACT显著提高了F1分数.
- 与标准的LLM提示 (DocRE方法) 相比,改善率为11.04% (CDR),9.10% (GDA) 和41.00% (ADE).
结论:
- 赛拉克特框架为高精度的BioDocRE提供了一个强大的解决方案.
- SyRACT有效地减轻了LLM的局限性,提高了生物医学关系提取中的性能和可解释性.
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