# Cross-Modal Attention Calibration for LVLM Hallucination Mitigation

arXiv: https://arxiv.org/abs/2501.01926 · HTML: https://mlsanity.com/paper/2501.01926/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Placed in Hallucination mitigation in LVLMs, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 39 of its 52 references that are in the map.
- First posted on arXiv 3 Jan 2025.

## Placed in

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

## Builds on (most cited first, 30 of 39)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [Evaluating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2305.10355/) (2023)
- [How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites](https://mlsanity.com/paper/2404.16821/) (2024)
- [mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality](https://mlsanity.com/paper/2304.14178/) (2023)
- [Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic](https://mlsanity.com/paper/2306.15195/) (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)
- [Contrastive Decoding: Open-ended Text Generation as Optimization](https://mlsanity.com/paper/2210.15097/) (2022)
- [mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration](https://mlsanity.com/paper/2311.04257/) (2023)
- [OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation](https://mlsanity.com/paper/2311.17911/) (2023)
- [Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning](https://mlsanity.com/paper/2306.14565/) (2023)
- [RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback](https://mlsanity.com/paper/2312.00849/) (2023)
- [Analyzing and Mitigating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2310.00754/) (2023)
- [Detecting and Preventing Hallucinations in Large Vision Language Models](https://mlsanity.com/paper/2308.06394/) (2023)
- [Multi-Modal Hallucination Control by Visual Information Grounding](https://mlsanity.com/paper/2403.14003/) (2024)
- [Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding](https://mlsanity.com/paper/2403.18715/) (2024)
- [Paying More Attention to Image: A Training-Free Method for Alleviating Hallucination in LVLMs](https://mlsanity.com/paper/2407.21771/) (2024)
- [HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding](https://mlsanity.com/paper/2403.00425/) (2024)
- [Silkie: Preference Distillation for Large Visual Language Models](https://mlsanity.com/paper/2312.10665/) (2023)
- [HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data](https://mlsanity.com/paper/2311.13614/) (2023)
- [Hallucination Augmented Contrastive Learning for Multimodal Large Language Model](https://mlsanity.com/paper/2312.06968/) (2023)

## Built on by (most cited first, 23 of 23)

- [From Perception to Cognition: A Survey of Vision-Language Interactive Reasoning in Multimodal Large Language Models](https://mlsanity.com/paper/2509.25373/) (2025)
- [Visual Attention Never Fades: Selective Progressive Attention ReCalibration for Detailed Image Captioning in Multimodal Large Language Models](https://mlsanity.com/paper/2502.01419/) (2025)
- [Make LVLMs Focus: Context-Aware Attention Modulation for Better Multimodal In-Context Learning](https://mlsanity.com/paper/2505.17097/) (2025)
- [OmniDPO: A Preference Optimization Framework to Address Omni-Modal Hallucination](https://mlsanity.com/paper/2509.00723/) (2025)
- [Hallucination Begins Where Saliency Drops](https://mlsanity.com/paper/2601.20279/) (2026)
- [Cross-Image Contrastive Decoding: Precise, Lossless Suppression of Language Priors in Large Vision-Language Models](https://mlsanity.com/paper/2505.10634/) (2025)
- [Mitigating Hallucination in Large Vision-Language Models through Aligning Attention Distribution to Information Flow](https://mlsanity.com/paper/2505.14257/) (2025)
- [On Epistemic Uncertainty of Visual Tokens for Object Hallucinations in Large Vision-Language Models](https://mlsanity.com/paper/2510.09008/) (2025)
- [Mobile-Agent-RAG: Driving Smart Multi-Agent Coordination with Contextual Knowledge Empowerment for Long-Horizon Mobile Automation](https://mlsanity.com/paper/2511.12254/) (2025)
- [Do LVLMs Know What They Know? A Systematic Study of Knowledge Boundary Perception in LVLMs](https://mlsanity.com/paper/2508.19111/) (2025)
- [CAI: Caption-Sensitive Attention Intervention for Mitigating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2506.23590/) (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)
- [SAGE: Sink-Aware Grounded Decoding for Multimodal Hallucination Mitigation](https://mlsanity.com/paper/2603.27898/) (2026)
- [CFPO: Counterfactual Policy Optimization for Multimodal Reasoning](https://mlsanity.com/paper/2606.23206/) (2026)
- [Not Blind but Silenced: Rebalancing Vision and Language via Adversarial Counter-Commonsense Equilibrium](https://mlsanity.com/paper/2605.10676/) (2026)
- [Attention to details, logits to truth: visual-aware attention and logits enhancement to mitigate hallucinations in LVLMs](https://mlsanity.com/paper/2602.09521/) (2026)
- [Visual Attention Drifts,but Anchors Hold:Mitigating Hallucination in Multimodal Large Language Models via Cross-Layer Visual Anchors](https://mlsanity.com/paper/2603.25088/) (2026)
- [CAST: Mitigating Object Hallucination in Large Vision-Language Models via Caption-Guided Visual Attention Steering](https://mlsanity.com/paper/2605.04641/) (2026)
- [Uncertainty-Aware Exploratory Direct Preference Optimization for Multimodal Large Language Models](https://mlsanity.com/paper/2605.04874/) (2026)
- [Test-Time Hallucination Control in Large Vision-Language Models](https://mlsanity.com/paper/2608.11474/) (2026)
- [CVE-SAI: Counterfactual Visual Evidence-Guided Selective Attribute Indexing for Risk-Controlled E-commerce Search](https://mlsanity.com/paper/2608.25023/) (2026)
- [HalluPrism: When Multimodal Uncertainty Should Diagnose, Not Decide](https://mlsanity.com/paper/2608.29193/) (2026)
- [Semantic-Spatial Agreement Verification for Mitigating Object Hallucination in Multimodal Large Language Models](https://mlsanity.com/paper/2609.17269/) (2026)

## Abstract (arXiv)

Large vision-language models (LVLMs) have shown remarkable capabilities in visual-language understanding. Despite their success, LVLMs still suffer from generating hallucinations in complex generation tasks, leading to inconsistencies between visual inputs and generated content. To address this issue, some approaches have introduced inference-time interventions, such as contrastive decoding, to reduce overreliance on language priors. However, these approaches overlook hallucinations stemming from position bias and spurious inter-modality correlations. In this paper, we propose a Cross-Modal Attention Calibration (CMAC) method to mitigate hallucinations in LVLMs in a training-free manner. In this method, we design an Inter-Modality Decoding (IMD) module to alleviate hallucination by a novel contrastive decoding mechanism. IMD masks the value vectors associated with significant cross-modal attention weights as distortion, which addresses both uni-modality overreliance and misleading inter-modality correlations. Additionally, a Cross-Modal Position Calibration (CMPC) module shrinks the position gap of image tokens, alleviating the position bias in cross-modal attention. Experimental results on diverse hallucination benchmarks validate the superiority of our method over existing state-of-the-art techniques in reducing hallucinations for LVLM. Our code will be available at https://github.com/lijm48/IMCCD.