@inproceedings{wang-etal-2026-lafact,
title = "{LAF}a{CT}: Attribution-based Localization and Focused Sequential Analysis of Fact-Critical Tokens for Hallucination Detection",
author = "Wang, Xin and
Li, Jiahao and
Zhang, Licheng and
Mao, Zhendong",
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.312/",
pages = "6877--6896",
ISBN = "979-8-89176-390-6",
abstract = "Large Language Models (LLMs) suffer from hallucinations, severely undermining their reliability. While white-box hallucination detection methods that leverage hidden states prevail, they fail to identify and focus on fact-critical information when analyzing token sequences. To address this, we propose LAFaCT, a Localize-then-Analyze detection framework. It first localizes fact-critical tokens using Factual Criticality, a novel metric derived from feature attribution. A subsequent stage then performs a focused sequential analysis on their hidden states. Extensive experiments on eight benchmarks and multiple model families confirm LAFaCT as the new state-of-the-art, with in-depth analyses validating the effectiveness of its core token-localization strategy."
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<abstract>Large Language Models (LLMs) suffer from hallucinations, severely undermining their reliability. While white-box hallucination detection methods that leverage hidden states prevail, they fail to identify and focus on fact-critical information when analyzing token sequences. To address this, we propose LAFaCT, a Localize-then-Analyze detection framework. It first localizes fact-critical tokens using Factual Criticality, a novel metric derived from feature attribution. A subsequent stage then performs a focused sequential analysis on their hidden states. Extensive experiments on eight benchmarks and multiple model families confirm LAFaCT as the new state-of-the-art, with in-depth analyses validating the effectiveness of its core token-localization strategy.</abstract>
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%0 Conference Proceedings
%T LAFaCT: Attribution-based Localization and Focused Sequential Analysis of Fact-Critical Tokens for Hallucination Detection
%A Wang, Xin
%A Li, Jiahao
%A Zhang, Licheng
%A Mao, Zhendong
%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 wang-etal-2026-lafact
%X Large Language Models (LLMs) suffer from hallucinations, severely undermining their reliability. While white-box hallucination detection methods that leverage hidden states prevail, they fail to identify and focus on fact-critical information when analyzing token sequences. To address this, we propose LAFaCT, a Localize-then-Analyze detection framework. It first localizes fact-critical tokens using Factual Criticality, a novel metric derived from feature attribution. A subsequent stage then performs a focused sequential analysis on their hidden states. Extensive experiments on eight benchmarks and multiple model families confirm LAFaCT as the new state-of-the-art, with in-depth analyses validating the effectiveness of its core token-localization strategy.
%U https://aclanthology.org/2026.acl-long.312/
%P 6877-6896
Markdown (Informal)
[LAFaCT: Attribution-based Localization and Focused Sequential Analysis of Fact-Critical Tokens for Hallucination Detection](https://aclanthology.org/2026.acl-long.312/) (Wang et al., ACL 2026)
ACL