诊断大模型在事实核查中如何权衡先验知识与证据，发现其决策不可靠。
Diagnosing LLM Arbitration Behavior over Pre-evidence Epistemic States in RAG-based Fact-Checking
- 按先验正确性与置信度将模型分为四类认知状态，构建诊断评估框架
- 七款模型表现不一，多数无法正确处理矛盾证据，存在严重依赖模型自身偏好
- 提出轻量级测试时仲裁方法，无需修改模型即可提升事实准确性
在基于RAG的事实核查中，大语言模型越来越多地被用作验证器，以核对给定陈述与检索到的证据。其参数化知识可能引发先验倾向，与检索到的上下文产生冲突，但现有评估框架未能刻画这种先验-上下文差异，也未衡量验证器在两者间的权衡行为。本文提出 extsc{PAVE}（Prior-Aware Verifier Evaluation），一个诊断测试平台，根据模型在证据前的先验正确性与置信度，将其划分为四种认知状态，并在此基准上评估其仲裁行为：是否在误导性证据下坚持正确的先验，以及在提供准确证据时能否纠正错误的先验。在七种LLM上的实验表明，先验-上下文仲裁行为不可靠且高度依赖模型，凸显了真实应用中验证器选择的重要性。基于此，我们提出一种轻量级的基于JSD的测试时仲裁方法，在不修改底层模型的前提下提升了事实可靠性，跨多种LLM家族表现优异。
原文摘要 · Abstract (English)
In RAG-based fact-checking, LLMs are increasingly used as verifiers to check given claims against retrieved evidence. Their parametric knowledge can induce pre-evidence tendencies that may conflict with the retrieved context, yet existing evaluation frameworks do not characterize such prior-context discrepancy or measure how verifiers arbitrate between parametric and contextual signals. We introduce \textsc{PAVE} (\emph{Prior-Aware Verifier Evaluation}), a diagnostic testbed that stratifies an LLM verifier into four epistemic states based on the correctness and confidence of its pre-evidence prior and evaluates its arbitration behavior on this new benchmark, i.e., whether it persists in correct prior under misleading evidence, and whether it corrects wrong prior when accurate evidence is provided. Experiments across seven LLMs reveal unreliable and highly model-dependent prior-context arbitration, highlighting the importance of verifier selection for real-world RAG-based fact-checking applications. Based on these findings, we propose a lightweight JSD-based test-time arbitration method that improves factual reliability without modifying the underlying model, achieving competitive performance across diverse LLM families.
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