推动大型语言模型在文档层面准确和全面地提取化学-疾病关系:一个评估研究
Mei Chen1,2, Tingting Zhang1,2, Shibin Wang1,2
1Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing 100081, China.
PloS one
|April 8, 2025
概括
大型语言模型显示出化学疾病关系提取的前景,达到高F1分数. 然而,它们的有效性受到数据稀缺和误解生物医学文本细微差别的倾向的限制.
科学领域:
- 生物医学自然语言处理
- 医疗保健中的人工智能
- 药物监督 药物监督 药物监督
背景情况:
- 化学疾病关系提取的深度学习受到有限的注释数据的阻碍.
- 现有的方法在精确的关系类型和相关因素的全面提取方面扎.
- 文档级提取需要对复杂的生物医学文本有着复杂的理解.
研究的目的:
- 评估GPT3.5,GPT4.0和Claude-opus对于文档级化学疾病关系的精确和全面的提取能力.
- 分析不同提示工程工作流对提取性能的影响.
- 调查大型语言模型 (LLM) 提取中的内容偏差和错误特征.
主要方法:
- 使用提示工程开发六个精确和五个全面的提取工作流程.
- 构建一个自我策划的数据集,以提取文档级别的化学-疾病关系.
- 在不同的工作流程和内容类型中对LLM绩效进行比较分析.
主要成果:
- 在精确提取方面,LLMs获得了87%的高F1分,在全面提取方面达到73%.
- 推动工程策略的有效性有限,这表明LLM在提取过程中的强.
- 法律学表现出内容偏见,在积极关系 (例如,诱导,加速) 上表现更好,并与隐含含义作斗争.
结论:
- 法律法学证明了化学疾病关系提取的巨大潜力,提供了实用的工作流程.
- 优化训练数据对于提高未来提取方法的准确性和效率至关重要.
- 解决LLM对隐性生物医学文本含义的解释是克服当前局限性的关键.
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