检测被省略关键信息的半真半假陈述，提升事实核查可信度
The Missing Parts: Augmenting Fact Verification with Half-Truth Detection
- 构建可识别信息缺失的再评估框架，结合证据对齐与意图推断
- 在1.5万条政治声明上测试，半真类别的准确率提升16点
- 适合做事实核查系统升级，尤其关注误导性信息的研究者
现有事实核查系统通常只判断陈述是否被检索到的证据支持，假设真实性仅取决于所陈述内容。然而现实中许多陈述是半真半假——看似正确但因省略关键背景而具有误导性。现有模型难以处理此类情况，因其未设计用于推理被省略的信息。为此，我们提出半真陈述检测任务，并构建了PolitiFact-Hidden基准数据集，包含1.5万条政治声明，每条均标注句子级证据对应关系及推断出的陈述意图。为应对挑战，我们提出TRACER框架，通过证据对齐、隐含意图推断和隐藏内容因果影响估计，识别基于信息缺失的误导性陈述。该框架可嵌入现有核查流程，在多个强基线模型上持续提升性能，显著提升半真类别F1值最高达16点，凸显建模信息缺失对可信核查的重要性。数据集与代码已公开于https://github.com/tangyixuan/TRACER。
原文摘要 · Abstract (English)
Fact verification systems typically assess whether a claim is supported by retrieved evidence, assuming that truthfulness depends solely on what is stated. However, many real-world claims are half-truths, factually correct yet misleading due to the omission of critical context. Existing models struggle with such cases, as they are not designed to reason about omitted information. We introduce the task of half-truth detection, and propose PolitiFact-Hidden, a new benchmark with 15k political claims annotated with sentence-level evidence alignment and inferred claim intent. To address this challenge, we present TRACER, a modular re-assessment framework that identifies omission-based misinformation by aligning evidence, inferring implied intent, and estimating the causal impact of hidden content. TRACER can be integrated into existing fact-checking pipelines and consistently improves performance across multiple strong baselines. Notably, it boosts Half-True classification F1 by up to 16 points, highlighting the importance of modeling omissions for trustworthy fact verification. The benchmark and code are available via https://github.com/tangyixuan/TRACER.
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