@inproceedings{kong-etal-2026-causalgaze,
title = "{C}ausal{G}aze: Unveiling Hallucinations via Counterfactual Graph Intervention in Large Language Models",
author = "Kong, Linggang and
Wu, Lei and
Zhang, Yunlong and
Zhong, Xiaofeng and
Zhen, Wang and
Wang, Yongjie and
Pan, Yao",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1943/",
pages = "39015--39028",
ISBN = "979-8-89176-395-1",
abstract = "Despite the groundbreaking advancements made by large language models (LLMs), hallucination remains a critical bottleneck for their deployment in high-stakes domains. Existing classification-based methods mainly rely on static and passive signals from internal states, which often captures the noise and spurious correlations, while overlooking the underlying causal mechanisms. To address this limitation, we shift the paradigm from passive observation to active intervention by introducing CausalGaze, a novel hallucination detection framework based on structural causal models (SCMs). CausalGaze models LLMs' internal states as dynamic causal graphs and employs counterfactual interventions to disentangle causal reasoning paths from incidental noise, thereby enhancing model interpretability. Extensive experiments across four datasets and three widely used LLMs demonstrate the effectiveness of CausalGaze, especially achieving over 5.2{\%} improvement in AUROC on the TruthfulQA dataset compared to state-of-the-art baselines."
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%0 Conference Proceedings
%T CausalGaze: Unveiling Hallucinations via Counterfactual Graph Intervention in Large Language Models
%A Kong, Linggang
%A Wu, Lei
%A Zhang, Yunlong
%A Zhong, Xiaofeng
%A Zhen, Wang
%A Wang, Yongjie
%A Pan, Yao
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F kong-etal-2026-causalgaze
%X Despite the groundbreaking advancements made by large language models (LLMs), hallucination remains a critical bottleneck for their deployment in high-stakes domains. Existing classification-based methods mainly rely on static and passive signals from internal states, which often captures the noise and spurious correlations, while overlooking the underlying causal mechanisms. To address this limitation, we shift the paradigm from passive observation to active intervention by introducing CausalGaze, a novel hallucination detection framework based on structural causal models (SCMs). CausalGaze models LLMs’ internal states as dynamic causal graphs and employs counterfactual interventions to disentangle causal reasoning paths from incidental noise, thereby enhancing model interpretability. Extensive experiments across four datasets and three widely used LLMs demonstrate the effectiveness of CausalGaze, especially achieving over 5.2% improvement in AUROC on the TruthfulQA dataset compared to state-of-the-art baselines.
%U https://aclanthology.org/2026.findings-acl.1943/
%P 39015-39028
Markdown (Informal)
[CausalGaze: Unveiling Hallucinations via Counterfactual Graph Intervention in Large Language Models](https://aclanthology.org/2026.findings-acl.1943/) (Kong et al., Findings 2026)
ACL
- Linggang Kong, Lei Wu, Yunlong Zhang, Xiaofeng Zhong, Wang Zhen, Yongjie Wang, and Yao Pan. 2026. CausalGaze: Unveiling Hallucinations via Counterfactual Graph Intervention in Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2026, pages 39015–39028, San Diego, California, United States. Association for Computational Linguistics.