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

ACL ARR 2026 January Submission6356 Authors

Keywords: fact-checking, hallucination detection, automatic evaluation, data synthesis, benchmark

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.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: fact checking

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Publicly available software and/or pre-trained models, Data resources

Languages Studied: English

Submission Number: 6356