Sieve and Sage：面向可靠RALM弃答的高效分心过滤
原标题：Sieve and Sage: Efficient Distraction Filtering for Reliable RALM Abstention
AI 摘要
该论文提出Sieve and Sage两阶段框架，用于提升检索增强语言模型（RALM）的弃答可靠性。作者将检索失败分解为「不可答状态」和「分心状态」，前者指缺乏必要证据，后者指相关证据与冲突、否定或对抗性信息混杂。框架用轻量级BERT分类器（Sieve）在调用昂贵LLM（Sage）前筛除分心证据，在通用和高风险专家领域评测中，系统准确率最高提升69.4个百分点，Macro-F1最高提升55.2个百分点，推理速度最高提升1.99倍。
正文节选
Sieve and Sage: Efficient Distraction Filtering for Reliable RALM Abstention Abstract Just as Socrates recognized the limits of his own knowledge, Retrieval-Augmented Language Models (RALMs) should learn to abstain when the retrieved evidence cannot support a reliable response. Existing approaches largely rely on monolithic LLMs to handle heterogeneous retrieval failures in a single step, resulting in limited abstention performance and high computational costs. We instead decompose retrieval fa