Verifying the Verifiers: Unveiling Pitfalls and Potentials in Fact Verifiers
Wooseok Seo, Seungju Han, Jaehun Jung, Benjamin Newman, Seungwon Lim, Seungbeen Lee, Ximing Lu, Yejin Choi, Youngjae Yu

TL;DR
This paper evaluates various large language models for fact verification, highlighting dataset issues, the effectiveness of frontier LLMs with few-shot learning, and the potential of fine-tuned smaller models with synthetic data for improved accuracy.
Contribution
It uncovers dataset annotation problems, emphasizes the importance of frontier LLMs in fact verification, and proposes training smaller models with synthetic data to enhance reasoning capabilities.
Findings
16% of dataset labels are ambiguous or incorrect, affecting model rankings.
Frontier LLMs with few-shot learning outperform many baselines.
Synthetic multi-hop reasoning data improves small model performance.
Abstract
Fact verification is essential for ensuring the reliability of LLM applications. In this study, we evaluate 12 pre-trained LLMs and one specialized fact-verifier, including frontier LLMs and open-weight reasoning LLMs, using a collection of examples from 14 fact-checking benchmarks. We share three findings intended to guide future development of more robust fact verifiers. First, we highlight the importance of addressing annotation errors and ambiguity in datasets, demonstrating that approximately 16\% of ambiguous or incorrectly labeled data substantially influences model rankings. Neglecting this issue may result in misleading conclusions during comparative evaluations, and we suggest using a systematic pipeline utilizing LLM-as-a-judge to help identify these issues at scale. Second, we discover that frontier LLMs with few-shot in-context examples, often overlooked in previous works,…
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Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling
