贝叶斯式RAG:不确定性意识检索可靠的财务问题回答
Lebede Ngartera1,2, Saralees Nadarajah3, Rodoumta Koina4
1AI Strategy-Innovation Specialist, TeraSystemsAI LLC, Philadelphia, PA, United States.
Frontiers in artificial intelligence
|February 12, 2026
概括
贝叶斯式RAG通过量化检索不确定性,提高准确性和减少幻觉来增强金融AI. 这种概率框架确保了AI在金融和医疗保健等高风险领域的可靠部署.
科学领域:
- 人工智能的人工智能
- 信息检索 信息检索
- 金融技术 金融技术
背景情况:
- 大型语言模型 (LLM) 经常提供事实上不正确的财务分析,冒着监管不合规和财务损失的风险.
- 传统的提取增强生成 (RAG) 缺乏不确定性量化,导致对复杂的财务查询过度自信但不可靠的AI输出.
研究的目的:
- 介绍贝叶斯式RAG,这是一个概率框架,用于将认识体系不确定性量化整合到信息检索中.
- 通过提高可靠性和准确性,解决在生产中部署人工智能的关键挑战,用于高风险的财务分析.
主要方法:
- 开发了一个贝叶斯式RAG框架,使用蒙特卡洛掉落来进行检索中的不确定性量化.
- 对查询和文档进行计算分布式嵌入,以实现不确定性意识的评分.
- 在果和微软2023 10-K财务报告上评估业绩.
主要成果:
- 与BM25基线相比,获得了93.1%的精度,精度@3 (+20.6%),MRR (+22.7%) 和NDCG@10 (+25.4%) 显著改善.
- 显示了26.8%更好的不确定性校准,并将AI幻觉减少了27.8%.
- 成功提取精确的财务数据,优于传统方法.
结论:
- 贝叶斯式RAG在检索系统中推进了不确定性量化,为不确定性意识的信息检索提供了理论基础.
- 模块化设计允许无集成到现有的RAG管道中,以便立即部署在具有风险意识的AI应用中.
- 提高AI在金融,医疗保健和监管合规方面的可靠性.
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