验证验证者：揭示事实验证器中的陷阱与潜力
Verifying the Verifiers: Unveiling Pitfalls and Potentials in Fact Verifiers
AI总结：
本研究评估了多种大语言模型和事实验证器，揭示了数据标注问题、前沿模型性能及小型验证器的改进潜力。
摘要：
事实验证对于确保大语言模型（LLM）应用的可靠性至关重要。在本研究中，我们评估了12个预训练的大语言模型和一个专门的事实验证器，包括前沿的大语言模型和开放式推理大语言模型，使用来自14个事实核查基准的示例集合。我们分享了三个发现，旨在指导未来开发更稳健的事实验证器。首先，我们强调了在数据集中解决标注错误和模糊性的重要性，证明大约16%的模糊或错误标注的数据显著影响模型排名。忽视这一问题可能导致比较评估中的误导性结论，我们建议使用一个系统化的流程利用LLM作为法官来帮助大规模识别这些问题。第二，我们发现具有少量示例的前沿大语言模型，往往在以前的工作中被忽视，实现了顶级性能。因此，我们建议未来的研究应包括与这些简单但高度有效的基线进行比较。最后，尽管这些前沿大语言模型效果显著，但它们带来了显著的成本，推动了小型、微调的事实验证器的发展。我们显示这些小型模型仍有改进的空间，特别是在需要复杂推理的实例上。令人鼓舞的是，我们证明通过在训练中加入合成多跳推理数据可以显著增强其在这些实例上的能力。我们发布了我们的代码、模型和数据集在https://github.com/just1nseo/verifying-the-verifiers。
英文摘要：
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, achieve top-tier performance. We therefore recommend that future studies include comparisons with these simple yet highly effective baselines. Lastly, despite their effectiveness, frontier LLMs incur substantial costs, motivating the development of small, fine-tuned fact verifiers. We show that these small models still have room for improvement, particularly on instances that require complex reasoning. Encouragingly, we demonstrate that augmenting training with synthetic multi-hop reasoning data significantly enhances their capabilities in such instances. We release our code, model, and dataset at https://github.com/just1nseo/verifying-the-verifiers.
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。
