@inproceedings{chen-etal-2026-vocabulary,
title = "Vocabulary Hijacking in {LVLM}s: Unveiling Critical Attention Heads by Excluding Inert Tokens to Mitigate Hallucination",
author = "Chen, Yangneng and
Li, Junlin and
Yao, Weijun and
Ma, Xilai and
DU, Guodong and
Wang, Wenya and
Li, Jing",
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.1782/",
pages = "38458--38485",
ISBN = "979-8-89176-390-6",
abstract = "Large Vision-Language Models (LVLMs) have achieved remarkable progress in multimodal tasks, yet their reliability is persistently undermined by hallucinations{---}generating text that contradicts visual input. Recent studies often attribute these errors to inadequate visual attention. In this work, we analyze the attention mechanisms via the logit lens, uncovering a distinct anomaly we term **Vocabulary Hijacking**. We discover that specific visual tokens, defined as **Inert Tokens**, disproportionately attract attention. Crucially, when their intermediate hidden states are projected into the vocabulary space, they consistently decode to a fixed set of unrelated words (termed **Hijacking Anchors**) across layers, revealing a rigid semantic collapse. Leveraging this semantic rigidity, we propose **Hijacking Anchor-Based Identification (HABI)**, a robust strategy to accurately localize these Inert Tokens. To quantify the impact of this phenomenon, we introduce the **Non-Hijacked Visual Attention Ratio (NHAR)**, a novel metric designed to identify attention heads that remain resilient to hijacking and are critical for factual accuracy. Building on these insights, we propose **Hijacking-Aware Visual Attention Enhancement (HAVAE)**, a training-free intervention that selectively strengthens the focus of these identified heads on salient visual content. Extensive experiments across multiple benchmarks demonstrate that HAVAE significantly mitigates hallucinations with **no additional computational overhead**, while preserving the model{'}s general capabilities."
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<abstract>Large Vision-Language Models (LVLMs) have achieved remarkable progress in multimodal tasks, yet their reliability is persistently undermined by hallucinations—generating text that contradicts visual input. Recent studies often attribute these errors to inadequate visual attention. In this work, we analyze the attention mechanisms via the logit lens, uncovering a distinct anomaly we term **Vocabulary Hijacking**. We discover that specific visual tokens, defined as **Inert Tokens**, disproportionately attract attention. Crucially, when their intermediate hidden states are projected into the vocabulary space, they consistently decode to a fixed set of unrelated words (termed **Hijacking Anchors**) across layers, revealing a rigid semantic collapse. Leveraging this semantic rigidity, we propose **Hijacking Anchor-Based Identification (HABI)**, a robust strategy to accurately localize these Inert Tokens. To quantify the impact of this phenomenon, we introduce the **Non-Hijacked Visual Attention Ratio (NHAR)**, a novel metric designed to identify attention heads that remain resilient to hijacking and are critical for factual accuracy. Building on these insights, we propose **Hijacking-Aware Visual Attention Enhancement (HAVAE)**, a training-free intervention that selectively strengthens the focus of these identified heads on salient visual content. Extensive experiments across multiple benchmarks demonstrate that HAVAE significantly mitigates hallucinations with **no additional computational overhead**, while preserving the model’s general capabilities.</abstract>
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%0 Conference Proceedings
%T Vocabulary Hijacking in LVLMs: Unveiling Critical Attention Heads by Excluding Inert Tokens to Mitigate Hallucination
%A Chen, Yangneng
%A Li, Junlin
%A Yao, Weijun
%A Ma, Xilai
%A DU, Guodong
%A Wang, Wenya
%A Li, Jing
%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 chen-etal-2026-vocabulary
%X Large Vision-Language Models (LVLMs) have achieved remarkable progress in multimodal tasks, yet their reliability is persistently undermined by hallucinations—generating text that contradicts visual input. Recent studies often attribute these errors to inadequate visual attention. In this work, we analyze the attention mechanisms via the logit lens, uncovering a distinct anomaly we term **Vocabulary Hijacking**. We discover that specific visual tokens, defined as **Inert Tokens**, disproportionately attract attention. Crucially, when their intermediate hidden states are projected into the vocabulary space, they consistently decode to a fixed set of unrelated words (termed **Hijacking Anchors**) across layers, revealing a rigid semantic collapse. Leveraging this semantic rigidity, we propose **Hijacking Anchor-Based Identification (HABI)**, a robust strategy to accurately localize these Inert Tokens. To quantify the impact of this phenomenon, we introduce the **Non-Hijacked Visual Attention Ratio (NHAR)**, a novel metric designed to identify attention heads that remain resilient to hijacking and are critical for factual accuracy. Building on these insights, we propose **Hijacking-Aware Visual Attention Enhancement (HAVAE)**, a training-free intervention that selectively strengthens the focus of these identified heads on salient visual content. Extensive experiments across multiple benchmarks demonstrate that HAVAE significantly mitigates hallucinations with **no additional computational overhead**, while preserving the model’s general capabilities.
%U https://aclanthology.org/2026.acl-long.1782/
%P 38458-38485
Markdown (Informal)
[Vocabulary Hijacking in LVLMs: Unveiling Critical Attention Heads by Excluding Inert Tokens to Mitigate Hallucination](https://aclanthology.org/2026.acl-long.1782/) (Chen et al., ACL 2026)
ACL
- Yangneng Chen, Junlin Li, Weijun Yao, Xilai Ma, Guodong DU, Wenya Wang, and Jing Li. 2026. Vocabulary Hijacking in LVLMs: Unveiling Critical Attention Heads by Excluding Inert Tokens to Mitigate Hallucination. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 38458–38485, San Diego, California, United States. Association for Computational Linguistics.