评估搜索引擎和大型语言模型来回答健康问题
Marcos Fernández-Pichel1, Juan C Pichel2, David E Losada2
1Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, Galicia, Spain. marcosfernandez.pichel@usc.es.
NPJ digital medicine
|March 11, 2025
概括
大型语言模型 (LLM) 在健康问题上表现优于传统搜索引擎 (SE). 提取增强 (RAG) 方法显著提高了LLM的准确性,特别是在较小的模型中.
科学领域:
- 信息科学 信息科学 信息科学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 搜索引擎 (SE) 是传统的信息搜索工具.
- 大型语言模型 (LLM) 正在成为问答的强大替代方案.
- 寻找健康信息带来了独特的挑战.
研究的目的:
- 为了比较SEs,LLMs和提取增强 (RAG) 变体的性能.
- 为了评估回答健康相关问题的准确性.
- 评估RAG对LLM绩效的影响.
主要方法:
- 评估了四种受欢迎的SE,七种LLM和RAG变体.
- 使用了TREC健康错误信息 (HM) 轨道的150个与健康有关的问题.
- 分析的准确性和快速的灵敏性.
主要成果:
- 电子工程师正确回答了50-70%的问题,但往往没有相关的结果.
- 课程的准确性更高,回答了大约80%的问题.
- RAG方法将较小的LLM精度提高了高达30%.
结论:
- 在回答健康问题方面,LLM表现优于SE.
- 拉格集成是提高LLM有效性的关键策略.
- 快速工程对于优化LLM性能至关重要.
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