Rave通过融合检索与可信度信号，提升虚假声明检测准确率。
RAVE: Retrieval and Scoring Aware Verifiable Claim Detection
- 结合证据检索与相关性、来源可信度的结构化信号
- 在CT22-test和PoliClaim-test上准确率与F1均优于基线
- 适合需要高精度事实核查的社交媒体内容审核场景
社交媒体上虚假信息的快速传播凸显了可扩展事实核查工具的必要性。关键一步是声明检测，即识别可客观验证的陈述。以往方法多依赖语言线索或声明可核查性，但在模糊的政治言论和多样格式（如推文）下表现不佳。我们提出RAVE（Retrieval and Scoring Aware Verifiable Claim Detection），该框架结合证据检索与相关性及来源可信度的结构化信号。在CT22-test和PoliClaim-test上的实验表明，RAVE在准确率和F1上均持续优于仅文本和基于检索的基线方法。
原文摘要 · Abstract (English)
The rapid spread of misinformation on social media underscores the need for scalable fact-checking tools. A key step is claim detection, which identifies statements that can be objectively verified. Prior approaches often rely on linguistic cues or claim check-worthiness, but these struggle with vague political discourse and diverse formats such as tweets. We present RAVE (Retrieval and Scoring Aware Verifiable Claim Detection), a framework that combines evidence retrieval with structured signals of relevance and source credibility. Experiments on CT22-test and PoliClaim-test show that RAVE consistently outperforms text-only and retrieval-based baselines in both accuracy and F1.
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