How Robust Are Automated Fact-Checking Systems? A Cross-Benchmark Evaluation Reveals Domain Shifts and Retrieval Bottlenecks
Score
77首次对完整两阶段AFC流程进行跨基准系统评估，填补现有研究空白，提供领域迁移与证据质量影响的量化结果。
研究设计严谨，涵盖多类型基线与顶级系统，对比维度全面，结果分析详实，但作为预印本尚需同行评审。
明确揭示检索瓶颈和领域敏感性问题，为提升事实核查系统及RAG应用提供可操作的改进方向，且代码数据开源。
论文于2026年发布，结合AVeriTeC 2025最新竞赛结果，反映当前AFC研究前沿。
来自arXiv预印本，作者提供代码和数据资源，但尚未经过正式期刊或会议同行评议。
Base 85 × T2 Source weight ×0.9 = 77
Curation line 70Selected
Why selected
The paper fills a gap in cross-domain AFC evaluation with highly reproducible findings, though as a preprint some details await further validation.
AI commentary
「This paper's value lies in hard data shattering the illusion of leaderboard universality—the same model can drop dramatically across domains. Beyond technical conclusions, it's a wake-up call for RAG and fact-checking engineering: if your retrieved evidence is noisy, even the strongest verifier won't save you. Teams doing real deployments should seriously study its domain-shift analysis and gold-evidence comparisons.」
Quick overview
This paper presents the first comprehensive cross-benchmark evaluation of automated fact-checking (AFC) systems, spanning four datasets across scientific, open-web, and climate domains, comparing nine models from random/sparse baselines to fine-tuned transformers, zero-shot LLMs, and top AVeriTeC 2025 systems. Findings show fine-tuned models outperform zero-shot LLMs and top systems on ClimateCheck; rankings are highly domain- and metric-dependent; replacing retrieved evidence with gold annotations improves veracity accuracy by 14-22 points, confirming retrieval remains the primary bottleneck. Code and datasets are released.
- Benchmarked nine AFC models across four datasets (scientific, open-web, climate), from baselines to AVeriTeC 2025 top systems.
- On ClimateCheck, fine-tuned models outperform zero-shot LLMs and AVeriTeC 2025 winners; noisy evidence degrades veracity prediction.
- Rankings are strongly domain- and metric-dependent: the best model on SciFact (macro-F1 0.70) drops to 0.31 on ClimateCheck.
- Replacing retrieved evidence with gold annotations improves veracity accuracy by 14-22 points, confirming retrieval as the primary bottleneck.
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