IDQuAD:传染病问题和答案数据集
Soonchan Kwon1, Sujeong Hur1, Beakcheol Jang1
1Graduate School of Information, Yonsei University, Seoul, South Korea.
PloS one
|October 9, 2025
概括
这项研究引入了一个新的数据集,IDQuAD,以改善传染病问题的大型语言模型 (LLM). 在IDQuAD上微调LLM显著提高了他们在传染病问题答案任务中的表现.
科学领域:
- 人工智能的人工智能
- 传染性疾病 传染性疾病
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 在各个领域都显示出前景,但它们对传染病任务的应用尚未得到充分发展.
- 现有的数据集缺乏培训和评估传染病查询的LLM所需的特异性.
研究的目的:
- 引入传染病问题和答案数据集 (IDQuAD) 用于培训和评估传染病背景下的LLM.
- 为了弥补LLM应用程序中的缺口,针对传染病特定的问题和答案.
主要方法:
- 使用医学论文,专利和新闻构建了IDQuAD.
- 采用了新的方法,如回答问题前生成和反事实思维,以提高QA对质量.
- 在IDQuAD上对Mistral-7B模型进行了微调,并评估了其在传染病质量控制任务中的性能.
主要成果:
- 微调的Mistral-7B型号显示出显著的性能改进,在一次性设置中,精确匹配 (EM) 得分从28.49%增加到65.47%.
- 微调的模型在各种设置和指标中实现了所有测试的LLM中最高的性能.
- 证明了IDQuAD在增强与传染病相关查询的LLM能力方面的有效性.
结论:
- IDQuAD作为一个基础数据集,用于利用LLMs推进传染病研究.
- 对像IDQuAD这样的专业数据集进行微调的LLM对于提高特定领域任务的性能是有效的.
- 这项工作为未来的数据集开发和传染病信息学LLM改进铺平了道路.
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