SEAC：基于经验条件的“停止还是澄清”主动事实核查方法
Stop or Clarify? Experience-Conditioned Active Fact Verification
AI 导读
arXiv 论文提出 SEAC（Sequential Experience-Adaptive Clarification），围绕固定事实核查器加装轻量级证据获取策略，解决核查前的“停止还是澄清”决策。
正文 · 原文
Abstract:Fact-verification benchmarks score the final label, while an interactive verifier must first decide whether the available evidence is sufficient. We focus on that intermediate Stop-or-Clarify Decision. Uncertainty alone is insufficient, because the benefit of another evidence-acquisition step is unknown before the step is taken. Sequential Experience-Adaptive Clarification (SEAC) wraps a fixed verifier with a lightweight evidence-acquisition policy. For each claim, SEAC retrieves evidence, runs the verifier, forms a label-free decision state, and combines stop risk with an observational clarification-utility estimate from earlier executed trajectories. Clarification is requested only when this score exceeds the action cost and decision threshold. On SciFact, LIAR, and FEVER, SEAC improves mean accuracy and macro-F1 over the compared fact-verification and active-retrieval baselines. Equal-budget and formula-level diagnostics support selective clarification as a contributor to the gain, while the small SciFact split leaves its paired improvement inconclusive.
| Subjects: | Artificial Intelligence (cs.AI) |
| ACM classes: | I.2.7 |
| Cite as: | arXiv:2610.34941 [cs.AI] |
| (or arXiv:2610.34941v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.34941 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yuhang Dong [view email]
[v1]
Mon, 26 Oct 2026 07:08:26 UTC (2,016 KB)
来源：arXiv cs.AI · arxiv.org