FactCorrector: A Graph-Inspired Approach to Long-Form Factuality Correction of Large Language Models

• URL: http://arxiv.org/abs/2601.11232v1
• Date: Fri, 16 Jan 2026 12:23:58 GMT
• Title: FactCorrector: A Graph-Inspired Approach to Long-Form Factuality Correction of Large Language Models
• Authors: Javier Carnerero-Cano, Massimiliano Pronesti, Radu Marinescu, Tigran Tchrakian, James Barry, Jasmina Gajcin, Yufang Hou, Alessandra Pascale, Elizabeth Daly,
• Abstract summary: Large language models (LLMs) are widely used in knowledge-intensive applications but often generate factually incorrect responses.<n>We introduce FactCorrector, a new post-hoc correction method that adapts across domains without retraining.<n> Experiments on VELI5 and several popular long-form factuality datasets show that the FactCorrector approach significantly improves factual precision while preserving relevance.
• Score: 47.782867391739195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) are widely used in knowledge-intensive applications but often generate factually incorrect responses. A promising approach to rectify these flaws is correcting LLMs using feedback. Therefore, in this paper, we introduce FactCorrector, a new post-hoc correction method that adapts across domains without retraining and leverages structured feedback about the factuality of the original response to generate a correction. To support rigorous evaluations of factuality correction methods, we also develop the VELI5 benchmark, a novel dataset containing systematically injected factual errors and ground-truth corrections. Experiments on VELI5 and several popular long-form factuality datasets show that the FactCorrector approach significantly improves factual precision while preserving relevance, outperforming strong baselines. We release our code at https://ibm.biz/factcorrector.

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