优化生物医学信息检索与一个关键字频率驱动的提示增强策略
Wasim Aftab1, Zivkos Apostolou2, Karim Bouazoune3
1Core Facility Bioinformatics, Biomedical Center, LMU Munich, Grosshaderner Str. 9, 82152, Martinsried, Germany. wasim.aftab@med.uni-muenchen.de.
BMC bioinformatics
|August 27, 2024
概括
这项研究通过使用显式查询信号来指导大型语言模型 (LLM) 来增强生物医学问答,提高响应的准确性并减少专业领域的幻觉.
科学领域:
- 生物医学信息学 生物医学信息学
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 生物医学文献挖掘是复杂的,因为跨学科的性质和术语.
- 早期的自然语言处理 (NLP) 很难处理细微的语言.
- 大型语言模型 (LLM) 提升了问答 (QA),但在专业领域面临着最新的,事实性的答案的挑战.
研究的目的:
- 提高生物医学质量保证任务中LLM的快速工程.
- 开发一个检索增强架构,以明确的用户查询信号指导LLM.
- 提高专业生物医学领域LLM产生的反应的准确性和相关性.
主要方法:
- 评估了两个提示增强方法:文本嵌入/矢量相似性和显式查询信号提取.
- 在50个具有挑战性的生物医学问题上对BM25基线进行测试的方法.
- 使用GPT-4进行响应生成和手动质量评估.
主要成果:
- 显式查询信号方法实现了0.95的中位精度@10,明显优于其他方法.
- 使用拟议方法的LLM生成的响应获得了2.5.5的质量中位数.
- 开发了WiseEuleQA机器人,用于比较分析和引文识别.
结论:
- 明确的用户查询信号优于文本嵌入,用于增强专业领域的LLM响应.
- 这种方法减轻了常见的LLM缺点,如幻觉和过时的信息.
- 为用户提供了对LLM使用的信息的更大控制权,以获得更可靠的QA.
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