Causal Evidence for Attention Head Imbalance in Modality Conflict Hallucination
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arXiv:2605.19250v1 Announce Type: new Abstract: Modality-conflict hallucination occurs when multimodal large language models (MLLMs) prioritize erroneous textual premises over contradictory visual evidence. To understand why visual evidence fails to prevail during generation, we take a mechanistic perspective and examine which internal components drive or resist this failure. We perform head-level causal analysis using path patching across five open-source MLLMs and identify two groups of attent
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Computer Science > Artificial Intelligence
[Submitted on 19 May 2026]
Causal Evidence for Attention Head Imbalance in Modality Conflict Hallucination
Jinrui Jiang, Zhangtai Wu, Zhen Wu, Xinyu Dai
Modality-conflict hallucination occurs when multimodal large language models (MLLMs) prioritize erroneous textual premises over contradictory visual evidence. To understand why visual evidence fails to prevail during generation, we take a mechanistic perspective and examine which internal components drive or resist this failure. We perform head-level causal analysis using path patching across five open-source MLLMs and identify two groups of attention heads with opposing causal roles: hallucination-driving heads and hallucination-resisting heads. We find a consistent asymmetry: driving effects are more broadly distributed and carry greater aggregate weight, whereas resisting effects concentrate in a small number of high-importance heads. Ablation experiments further confirm that these groups exert opposing effects during generation: distributed driving influence and localized resistance together form an imbalanced routing structure that biases generation toward the erroneous premise. Motivated by this finding, we propose MACI (Modality-conflict-Aware Causal Intervention), a conditional intervention that suppresses causally identified hallucination-driving heads only when conflict is detected. Across five MLLMs, MACI achieves the largest hallucination reduction among compared inference-time baselines on the MMMC benchmark with a favorable hallucination-accuracy trade-off, and transfers zero-shot to the SCI-SemanticConflict test.
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.19250 [cs.AI]
(or arXiv:2605.19250v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.19250
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From: Jinrui Jiang [view email]
[v1] Tue, 19 May 2026 01:47:53 UTC (311 KB)
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