Causal Evidence for Attention Head Imbalance in Modality Conflict Hallucination
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 conce...
| Authors | Jinrui Jiang, Zhangtai Wu, Zhen Wu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 19 May 2026 |
| Last updated at source | 19 May 2026 |
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