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证据充分性边界训练：面向多跳问答的选择性回答方法
原标题：Learning Evidence Sufficiency Boundaries for Selective Answering in Grounded Multi-Hop QA
AI 摘要
该研究提出证据充分性边界训练框架，用于多跳问答中的选择性回答。框架构建四级证据链，训练模型在证据不足时输出<ABSTAIN>，证据充分时给出答案。基于Qwen2.5-3B-Instruct的实验显示，该方法在边界定位和外部不可回答集上的无支撑回答率均优于基线，同时保持竞争性的QA F1分数。
以上摘要由 AI 生成，可能存在误差。事实请以原文为准。
正文节选
Learning Evidence Sufficiency Boundaries for Selective Answering in Grounded Multi-Hop QA Abstract Grounded question answering systems should answer only when the supplied evidence supports the answer. In multi-hop QA, this requirement is difficult because partial evidence can make an unsupported answer appear plausible. We study selective answering through evidence sufficiency boundaries: for the same question, a model should abstain under unsupported or partially supported context, answer when
发布时间：2026-09-03 12:00
抓取时间：2026-09-03 18:02
来源机构：arXiv