@inproceedings{carnerero-cano-etal-2026-factcorrector,
title = "{F}act{C}orrector: A Graph-Inspired Approach to Long-Form Factuality Correction of Large Language Models",
author = "Carnerero-Cano, Javier and
Pronesti, Massimiliano and
Marinescu, Radu and
Tchrakian, Tigran T. and
Barry, James and
Gajcin, Jasmina and
Hou, Yufang and
Pascale, Alessandra and
Daly, Elizabeth M.",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.2147/",
pages = "46278--46307",
ISBN = "979-8-89176-390-6",
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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%0 Conference Proceedings
%T FactCorrector: A Graph-Inspired Approach to Long-Form Factuality Correction of Large Language Models
%A Carnerero-Cano, Javier
%A Pronesti, Massimiliano
%A Marinescu, Radu
%A Tchrakian, Tigran T.
%A Barry, James
%A Gajcin, Jasmina
%A Hou, Yufang
%A Pascale, Alessandra
%A Daly, Elizabeth M.
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F carnerero-cano-etal-2026-factcorrector
%X 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.
%U https://aclanthology.org/2026.acl-long.2147/
%P 46278-46307
Markdown (Informal)
[FactCorrector: A Graph-Inspired Approach to Long-Form Factuality Correction of Large Language Models](https://aclanthology.org/2026.acl-long.2147/) (Carnerero-Cano et al., ACL 2026)
ACL
- Javier Carnerero-Cano, Massimiliano Pronesti, Radu Marinescu, Tigran T. Tchrakian, James Barry, Jasmina Gajcin, Yufang Hou, Alessandra Pascale, and Elizabeth M. Daly. 2026. FactCorrector: A Graph-Inspired Approach to Long-Form Factuality Correction of Large Language Models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 46278–46307, San Diego, California, United States. Association for Computational Linguistics.