# Cracking the Code of Hallucination in LVLMs with Vision-aware Head Divergence

arXiv: https://arxiv.org/abs/2412.13949 · HTML: https://mlsanity.com/paper/2412.13949/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Placed in Hallucination detection and mitigation, one of 17 directions in LLM reasoning.
- Builds on 17 of its 29 references that are in the map.
- First posted on arXiv 18 Dec 2024.

## Placed in

- LLM reasoning / [Hallucination detection and mitigation](https://mlsanity.com/lr/hallucination-detection-and-mitigation/)
- Multimodal LLMs / vision-language models / [Hallucination mitigation in LVLMs](https://mlsanity.com/mllm/hallucination-mitigation-in-lvlms/)

## Builds on (most cited first, 17 of 17)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [Inference-Time Intervention: Eliciting Truthful Answers from a Language Model](https://mlsanity.com/paper/2306.03341/) (2023)
- [Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding](https://mlsanity.com/paper/2311.16922/) (2023)
- [Object Hallucination in Image Captioning](https://mlsanity.com/paper/1809.02156/) (2018)
- [OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation](https://mlsanity.com/paper/2311.17911/) (2023)
- [RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback](https://mlsanity.com/paper/2312.00849/) (2023)
- [Counterfactual VQA: A Cause-Effect Look at Language Bias](https://mlsanity.com/paper/2006.04315/) (2020)
- [AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models](https://mlsanity.com/paper/2410.02355/) (2024)
- [Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference Optimization](https://mlsanity.com/paper/2311.16839/) (2023)
- [Seeing Clearly by Layer Two: Enhancing Attention Heads to Alleviate Hallucination in LVLMs](https://mlsanity.com/paper/2411.09968/) (2024)
- [DAMRO: Dive into the Attention Mechanism of LVLM to Reduce Object Hallucination](https://mlsanity.com/paper/2410.04514/) (2024)
- [CODE: Contrasting Self-generated Description to Combat Hallucination in Large Multi-modal Models](https://mlsanity.com/paper/2406.01920/) (2024)
- [Visual Description Grounding Reduces Hallucinations and Boosts Reasoning in LVLMs](https://mlsanity.com/paper/2405.15683/) (2024)
- [Do Vision & Language Decoders use Images and Text equally? How Self-consistent are their Explanations?](https://mlsanity.com/paper/2404.18624/) (2024)

## Built on by (most cited first, 19 of 19)

- [Reinforced Lifelong Editing for Language Models](https://mlsanity.com/paper/2502.05759/) (2025)
- [A Survey of Multimodal Hallucination Evaluation and Detection](https://mlsanity.com/paper/2507.19024/) (2025)
- [When Seeing Overrides Knowing: Disentangling Knowledge Conflicts in Vision-Language Models](https://mlsanity.com/paper/2507.13868/) (2025)
- [Tell Model Where to Look: Mitigating Hallucinations in MLLMs by Vision-Guided Attention](https://mlsanity.com/paper/2511.20032/) (2025)
- [Vision-Language Introspection: Mitigating Overconfident Hallucinations in MLLMs via Interpretable Bi-Causal Steering](https://mlsanity.com/paper/2601.05159/) (2026)
- [VIB-Probe: Detecting and Mitigating Hallucinations in Vision-Language Models via Variational Information Bottleneck](https://mlsanity.com/paper/2601.05547/) (2026)
- [Revealing Perception and Generation Dynamics in LVLMs: Mitigating Hallucinations via Validated Dominance Correction](https://mlsanity.com/paper/2512.18813/) (2025)
- [Capturing Gaze Shifts for Guidance: Cross-Modal Fusion Enhancement for VLM Hallucination Mitigation](https://mlsanity.com/paper/2510.22067/) (2025)
- [Cross-Layer Vision Smoothing: Enhancing Visual Understanding via Sustained Focus on Key Objects in Large Vision-Language Models](https://mlsanity.com/paper/2509.12897/) (2025)
- [ESMC: MLLM-Based Embedding Selection for Explainable Multiple Clustering](https://mlsanity.com/paper/2512.00725/) (2025)
- [When RAG Hurts: Diagnosing and Mitigating Attention Distraction in Retrieval-Augmented LVLMs](https://mlsanity.com/paper/2602.00344/) (2026)
- [Beyond Static Cropping: Layer-Adaptive Visual Localization and Decoding Enhancement](https://mlsanity.com/paper/2602.04304/) (2026)
- [Causal Evidence for Attention Head Imbalance in Modality Conflict Hallucination](https://mlsanity.com/paper/2605.19250/) (2026)
- [Segmentation-Based Attention Entropy: Detecting and Mitigating Object Hallucinations in Large Vision-Language Models](https://mlsanity.com/paper/2603.16558/) (2026)
- [Revealing Multi-View Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2603.23934/) (2026)
- [Mitigating Action-Relation Hallucinations in LVLMs via Relation-aware Visual Enhancement](https://mlsanity.com/paper/2605.11808/) (2026)
- [Role-Break in Attention Heads: Understanding and Detecting Hallucinations in VLMs](https://mlsanity.com/paper/2607.29412/) (2026)
- [MLLMs Hallucinate when Information Distribution Drifts in Synergy Heads](https://mlsanity.com/paper/2609.09206/) (2026)
- [OPD-Aha: From Linguistic Momentum to Visual Reflection in Multimodal On-Policy Distillation](https://mlsanity.com/paper/2609.16459/) (2026)

## Abstract (arXiv)

Large vision-language models (LVLMs) have made substantial progress in integrating large language models (LLMs) with visual inputs, enabling advanced multimodal reasoning. Despite their success, a persistent challenge is hallucination-where generated text fails to accurately reflect visual content-undermining both accuracy and reliability. Existing methods focus on alignment training or decoding refinements but primarily address symptoms at the generation stage without probing the underlying causes. In this work, we investigate the internal mechanisms driving hallucination in LVLMs, with an emphasis on the multi-head attention module. Specifically, we introduce Vision-aware Head Divergence (VHD), a metric that quantifies the sensitivity of attention head outputs to visual context. Based on this, our findings reveal the presence of vision-aware attention heads that are more attuned to visual information; however, the model's overreliance on its prior language patterns is closely related to hallucinations. Building on these insights, we propose Vision-aware Head Reinforcement (VHR), a training-free approach to mitigate hallucination by enhancing the role of vision-aware attention heads. Extensive experiments demonstrate that our method achieves superior performance compared to state-of-the-art approaches in mitigating hallucinations, while maintaining high efficiency with negligible additional time overhead.