Learning Evidence Sufficiency Boundaries for Selective Answering in Grounded Multi-Hop QA
This paper introduces Evidence Sufficiency Boundary Training for grounded multi-hop QA. Models are trained to abstain when evidence is unsupported or partial, answer when evidence first becomes sufficient, and remain stable as redundant evidence arrives. Built from HotpotQA, 2WikiMultiHopQA, and MuSiQue evidence chains, the method with Qwen2.5-3B-Instruct and LoRA achieves a flip accuracy of 0.807 versus 0.781 for a token-level abstention baseline, and the lowest unsupported-answer rate of 0.095 on external non-answerable sets.
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[Submitted on 1 Sep 2026]
Title:Learning Evidence Sufficiency Boundaries for Selective Answering in Grounded Multi-Hop QA
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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 the context first becomes sufficient, and keep the answer stable when redundant evidence is added. We introduce Evidence Sufficiency Boundary Training, a generation-native training framework that constructs ordered evidence chains and supervises the abstain-to-answer transition directly. The method combines level supervision, a boundary flip margin, post-boundary stability, and answer recall protection. We build evidence chains from HotpotQA, 2WikiMultiHopQA, and MuSiQue, then evaluate models with chain metrics, raw QA utility, and unsupported-answer rates on external non-answerable sets. With Qwen2.5-3B-Instruct and LoRA adaptation, Evidence Sufficiency Boundary Training gives the strongest boundary localization among the tested systems, with flip accuracy of 0.807 compared with 0.781 for a token-level abstention baseline. It also achieves the lowest overall unsupported-answer rate on external non-answerable evaluation, 0.095 compared with 0.101 for the same baseline, while retaining competitive raw QA F1. The results show that grounded selective answering improves when training marks the evidence level where refusal should give way to answering.
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2609.01687 [cs.CL]
(or arXiv:2609.01687v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.01687
arXiv-issued DOI via DataCite
Submission history
From: Haruto Sato [view email] [v1] Tue, 1 Sep 2026 15:16:11 UTC (1,301 KB)
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