PASS-FC通过动态搜索提升复杂事实核查准确率
PASS-FC: Progressive and Adaptive Search Scheme for Fact Checking of Comprehensive Claims
- 分步构建时间锚点与实体消歧，动态生成查询并跨语言扩展
- 在六大数据集上超越大模型基线，多语言性能与语言亲缘性相关
- 适合需要高精度、跨语言事实验证的研究与应用
自动化事实核查仍难以应对时效性强、实体模糊或被噪声结果掩盖的复杂声明。本文提出PASS-FC：一种渐进式自适应搜索方案。每个原子声明首先通过精确时间范围和实体消歧描述进行定位；随后，自适应搜索循环生成结构化查询，通过可信源筛选域名，并实现跨语言查询扩展；必要时轻量级反思机制重启循环。在六个基准数据集（涵盖通用知识、科学文献、现实事件及十种语言）上的实验表明，PASS-FC持续优于现有系统，甚至超过使用更大骨干语言模型的方案。在多语言X-FACT数据集上，各语言表现部分与英语的类型学相似性相关，强制模型在低资源语言中推理会降低准确率。消融实验凸显时间定位与自适应搜索的重要性，详细分析显示跨语言检索能提供真实新增证据。代码与完整结果将公开，以促进后续研究。
原文摘要 · Abstract (English)
Automated fact-checking (AFC) still falters on claims that are time-sensitive, entity-ambiguous, or buried beneath noisy search-engine results. We present PASS-FC, a Progressive and Adaptive Search Scheme for Fact Checking. Each atomic claim is first grounded with a precise time span and disambiguated entity descriptors. An adaptive search loop then issues structured queries, filters domains through credible-source selection, and expands queries cross-lingually; when necessary, a lightweight reflection routine restarts the loop. Experiments on six benchmark--covering general knowledge, scientific literature, real-world events, and ten languages--show that PASS-FC consistently outperforms prior systems, even those powered by larger backbone LLMs. On the multilingual X-FACT set, performance of different languages partially correlates with typological closeness to English, and forcing the model to reason in low-resource languages degrades accuracy. Ablations highlight the importance of temporal grounding and the adaptive search scheme, while detailed analysis shows that cross-lingual retrieval contributes genuinely new evidence. Code and full results will be released to facilitate further research.
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