PASS-FC：面向综合主张事实核查的渐进式自适应搜索方案
PASS-FC: Progressive and Adaptive Search Scheme for Fact Checking of Comprehensive Claims
AI总结：
针对事实核查中时间敏感、实体模糊等难题，提出渐进式自适应搜索方案PASS-FC，通过时间接地、自适应查询与跨语言检索，在六个基准上超越现有系统。
摘要：
自动化事实核查（AFC）在处理对时间敏感、实体模糊或淹没在嘈杂搜索引擎结果中的主张时仍表现不佳。我们提出PASS-FC，一种用于事实核查的渐进式自适应搜索方案。每个原子主张首先通过精确的时间跨度和消歧后的实体描述符进行接地。随后，一个自适应搜索循环发出结构化查询，通过可信来源选择过滤域名，并跨语言扩展查询；必要时，一个轻量级反思例程会重启该循环。在覆盖常识、科学文献、真实世界事件及十种语言的六个基准上的实验表明，PASS-FC始终优于先前系统，包括那些由更大规模骨干大语言模型驱动的系统。在多语言X-FACT数据集上，不同语言的性能部分地与英语的类型学接近程度相关，而强制模型用低资源语言推理会降低准确率。消融实验凸显了时间接地和自适应搜索方案的重要性，而详细分析表明跨语言检索贡献了真正的新证据。代码和完整结果将发布以促进进一步研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
