区分歧义与答案多样性，提升开放域问答的准确与效率
Diversity is Not Ambiguity: Toward Accurate and Efficient Ambiguity Detection for Open-Domain QA
- 通过逻辑冲突检测歧义，而非简单看答案多样性
- 在4703个查询上提升歧义检测F1最高达10.4%
- 比最佳对手快16倍，适合需要高效推理的系统
开放域问答中如何判断问题是否歧义？现有方法常将答案多样性误认为歧义，导致误判。同时处理方式统一，浪费计算资源。本文提出ARCHIVE框架，基于逻辑冲突判定歧义：当所有有效答案无法在同一解释下同时为真时，问题即为歧义。该框架结合轻量级早期退出编码器与冲突推理模块，利用不变性目标增强对噪声答案集的鲁棒性。构建了包含4703个查询的QuireQA基准，涵盖事实类、非事实类及无效问题。实验表明，ARCHIVE在歧义检测上F1-amb最高提升10.4%，非歧义检测F1-unamb最高提升21.6%，且运行速度比最优竞争模型快16倍。
原文摘要 · Abstract (English)
How can question answering (QA) systems determine whether a query is ambiguous? Ambiguity detection is essential in open-domain QA, as misclassification leads to answering the wrong interpretation or unnecessary clarification. However, existing methods conflate answer diversity with ambiguity, leading to inaccurate predictions. They also process queries uniformly, resulting in wasteful computation. We propose ARCHIVE (Ambiguity Recognition via Cascaded Hypothesis Inspection and Conflict Verification), an accurate and efficient framework that detects ambiguity via logical conflict: a query is ambiguous when its valid answers cannot all be true under a single interpretation. ARCHIVE combines a lightweight early-exit encoder for surface-detectable cases with a conflict reasoning module that models logical relations among answers, reinforced by an invariance objective for robustness to noisy answer sets. We present QuireQA, a 4,703-query benchmark spanning factoid, non-factoid, and ill-formed queries. Experiments show ARCHIVE outperforms competitors, improving F1-amb by up to 10.4% and F1-unamb by up to 21.6%, while operating 16$\times$ faster than the best competitor.
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