# PerturboLLaVA: Reducing Multimodal Hallucinations with Perturbative Visual Training

arXiv: https://arxiv.org/abs/2503.06486 · HTML: https://mlsanity.com/paper/2503.06486/

## Key facts

- Cited by 38 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 34 of its 44 references that are in the map.
- First posted on arXiv 9 Mar 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 34)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [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)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [MMBench: Is Your Multi-modal Model an All-around Player?](https://mlsanity.com/paper/2307.06281/) (2023)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [Evaluating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2305.10355/) (2023)
- [ShareGPT4V: Improving Large Multi-Modal Models with Better Captions](https://mlsanity.com/paper/2311.12793/) (2023)
- [SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension](https://mlsanity.com/paper/2307.16125/) (2023)
- [DeepSeek-VL: Towards Real-World Vision-Language Understanding](https://mlsanity.com/paper/2403.05525/) (2024)
- [MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies](https://mlsanity.com/paper/2404.06395/) (2024)
- [Aligning Large Multimodal Models with Factually Augmented RLHF](https://mlsanity.com/paper/2309.14525/) (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)
- [DeepSeek LLM: Scaling Open-Source Language Models with Longtermism](https://mlsanity.com/paper/2401.02954/) (2024)
- [HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models](https://mlsanity.com/paper/2310.14566/) (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)
- [Analyzing and Mitigating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2310.00754/) (2023)
- [AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation](https://mlsanity.com/paper/2311.07397/) (2023)
- [InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition](https://mlsanity.com/paper/2309.15112/) (2023)
- [Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases](https://mlsanity.com/paper/1909.03683/) (2019)
- [Woodpecker: Hallucination Correction for Multimodal Large Language Models](https://mlsanity.com/paper/2310.16045/) (2023)
- [Building and better understanding vision-language models: insights and future directions](https://mlsanity.com/paper/2408.12637/) (2024)

## Built on by (most cited first, 30 of 38)

- [Seeing but Not Believing: Probing the Disconnect Between Visual Attention and Answer Correctness in VLMs](https://mlsanity.com/paper/2510.17771/) (2025)
- [MCA-LLaVA: Manhattan Causal Attention for Reducing Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2507.09184/) (2025)
- [On Robustness and Chain-of-Thought Consistency of RL-Finetuned VLMs](https://mlsanity.com/paper/2602.12506/) (2026)
- [OmniDPO: A Preference Optimization Framework to Address Omni-Modal Hallucination](https://mlsanity.com/paper/2509.00723/) (2025)
- [Tell Model Where to Look: Mitigating Hallucinations in MLLMs by Vision-Guided Attention](https://mlsanity.com/paper/2511.20032/) (2025)
- [Cross-Image Contrastive Decoding: Precise, Lossless Suppression of Language Priors in Large Vision-Language Models](https://mlsanity.com/paper/2505.10634/) (2025)
- [JEEM: Vision-Language Understanding in Four Arabic Dialects](https://mlsanity.com/paper/2503.21910/) (2025)
- [Not All Tokens and Heads Are Equally Important: Dual-Level Attention Intervention for Hallucination Mitigation](https://mlsanity.com/paper/2506.12609/) (2025)
- [MemDreamer: Decoupling Perception and Reasoning for Long Video Understanding via Hierarchical Graph Memory and Agentic Retrieval Mechanism](https://mlsanity.com/paper/2606.07512/) (2026)
- [MoD-DPO: Towards Mitigating Cross-modal Hallucinations in Omni LLMs using Modality Decoupled Preference Optimization](https://mlsanity.com/paper/2603.03192/) (2026)
- [Android Coach: Improve Online Agentic Training Efficiency with Single State Multiple Actions](https://mlsanity.com/paper/2604.07277/) (2026)
- [Capturing Gaze Shifts for Guidance: Cross-Modal Fusion Enhancement for VLM Hallucination Mitigation](https://mlsanity.com/paper/2510.22067/) (2025)
- [InEx: Hallucination Mitigation via Introspection and Cross-Modal Multi-Agent Collaboration](https://mlsanity.com/paper/2512.02981/) (2025)
- [GraphThinker: Reinforcing Temporally Grounded Video Reasoning with Event Graph Thinking](https://mlsanity.com/paper/2602.17555/) (2026)
- [Taming Hallucinations: Boosting MLLMs' Video Understanding via Counterfactual Video Generation](https://mlsanity.com/paper/2512.24271/) (2025)
- [Do MLLMs Really See It: Reinforcing Visual Attention in Multimodal LLMs](https://mlsanity.com/paper/2602.08241/) (2026)
- [Revealing and Enhancing Core Visual Regions: Harnessing Internal Attention Dynamics for Hallucination Mitigation in LVLMs](https://mlsanity.com/paper/2602.15556/) (2026)
- [FINER: MLLMs Hallucinate under Fine-grained Negative Queries](https://mlsanity.com/paper/2603.17662/) (2026)
- [FREAK: A Fine-grained Hallucination Evaluation Benchmark for Advanced MLLMs](https://mlsanity.com/paper/2603.19765/) (2026)
- [Grounding or Guessing? Visual Signals for Detecting Hallucinations in Sign Language Translation](https://mlsanity.com/paper/2510.18439/) (2025)
- [Towards Fine-Grained Human Motion Video Captioning](https://mlsanity.com/paper/2510.24767/) (2025)
- [Mitigating Object and Action Hallucinations in Multimodal LLMs via Self-Augmented Contrastive Alignment](https://mlsanity.com/paper/2512.04356/) (2025)
- [Evidence-RL: Towards Evidence-intensive Visual Reasoning](https://mlsanity.com/paper/2608.08021/) (2026)
- [Investigating and Enhancing the Robustness of Large Multimodal Models Against Temporal Inconsistency](https://mlsanity.com/paper/2505.14405/) (2025)
- [Anatomical Region-Guided Contrastive Decoding: A Plug-and-Play Strategy for Mitigating Hallucinations in Medical VLMs](https://mlsanity.com/paper/2512.17189/) (2025)
- [Watch Closely: Mitigating Object Hallucinations in Large Vision-Language Models with Disentangled Decoding](https://mlsanity.com/paper/2512.19070/) (2025)
- [TGIF: Text-Guided Layer Fusion Mitigates Hallucination in Multimodal LLMs](https://mlsanity.com/paper/2601.03100/) (2026)
- [Curing Semantic Drift: A Dynamic Approach to Grounding Generation in Large Vision-Language Models](https://mlsanity.com/paper/2506.21509/) (2025)
- [HII-DPO: Eliminate Hallucination via Accurate Hallucination-Inducing Counterfactual Images](https://mlsanity.com/paper/2602.10425/) (2026)
- [Are Android GUI Agents Robust Against Runtime Anomalies? AnTrap: Evaluating Agents in Dynamic Adversarial Environments](https://mlsanity.com/paper/2608.24099/) (2026)

## Abstract (arXiv)

This paper aims to address the challenge of hallucinations in Multimodal Large Language Models (MLLMs) particularly for dense image captioning tasks. To tackle the challenge, we identify the current lack of a metric that finely measures the caption quality in concept level. We hereby introduce HalFscore, a novel metric built upon the language graph and is designed to evaluate both the accuracy and completeness of dense captions at a granular level. Additionally, we identify the root cause of hallucination as the model's over-reliance on its language prior. To address this, we propose PerturboLLaVA, which reduces the model's reliance on the language prior by incorporating adversarially perturbed text during training. This method enhances the model's focus on visual inputs, effectively reducing hallucinations and producing accurate, image-grounded descriptions without incurring additional computational overhead. PerturboLLaVA significantly improves the fidelity of generated captions, outperforming existing approaches in handling multimodal hallucinations and achieving improved performance across general multimodal benchmarks.