InFi-Check: Interpretable and Fine-Grained Fact-Checking of LLMs

• URL: http://arxiv.org/abs/2601.06666v1
• Date: Sat, 10 Jan 2026 20:00:17 GMT
• Title: InFi-Check: Interpretable and Fine-Grained Fact-Checking of LLMs
• Authors: Yuzhuo Bai, Shuzheng Si, Kangyang Luo, Qingyi Wang, Wenhao Li, Gang Chen, Fanchao Qi, Maosong Sun,
• Abstract summary: InFi-Check is a framework for interpretable and fine-grained fact-checking of large language models.<n>InFi-Checker provides supporting evidence, classify fine-grained error types, and produce justifications along with corrections.<n>Experiments show that InFi-Checker achieves state-of-the-art performance on InFi-Check-FG.
• Score: 48.98720183285795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) often hallucinate, yet most existing fact-checking methods treat factuality evaluation as a binary classification problem, offering limited interpretability and failing to capture fine-grained error types. In this paper, we introduce InFi-Check, a framework for interpretable and fine-grained fact-checking of LLM outputs. Specifically, we first propose a controlled data synthesis pipeline that generates high-quality data featuring explicit evidence, fine-grained error type labels, justifications, and corrections. Based on this, we further construct large-scale training data and a manually verified benchmark InFi-Check-FG for fine-grained fact-checking of LLM outputs. Building on these high-quality training data, we further propose InFi-Checker, which can jointly provide supporting evidence, classify fine-grained error types, and produce justifications along with corrections. Experiments show that InFi-Checker achieves state-of-the-art performance on InFi-Check-FG and strong generalization across various downstream tasks, significantly improving the utility and trustworthiness of factuality evaluation.

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