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PerturboLLaVA: Reducing Multimodal Hallucinations with Perturbative Visual Training

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arxiv 2503.06486 v1 pith:U6EDSYK3 submitted 2025-03-09 cs.CV cs.AI

Cong Chen , Mingyu Liu , Chenchen Jing , Yizhou Zhou , Fengyun Rao , Hao Chen , Bo Zhang , Chunhua Shen This is my paper
classification cs.CVcs.AI
keywords hallucinationslanguagemultimodalmodelperturbollavaaddresscaptionschallenge
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. C-PTQ: Fisher-weighted Channel-wise Sensitivity for Post-training Quantization of MLLMs

    cs.CV 2026-07 conditional novelty 6.0 of 10

    C-PTQ weights quantization error by per-channel Fisher information of the task loss, improving low-bit accuracy of multimodal LLMs by small margins over existing channel-wise scaling methods.

  2. GraphThinker: Reinforcing Temporally Grounded Video Reasoning with Event Graph Thinking

    cs.CV 2026-02 unverdicted novelty 6.0 of 10

    GraphThinker reduces temporal hallucinations in video reasoning by constructing event-based scene graphs and applying visual attention rewards in reinforcement finetuning.

  3. Capturing Gaze Shifts for Guidance: Cross-Modal Fusion Enhancement for VLM Hallucination Mitigation

    cs.CV 2025-10 conditional novelty 6.0 of 10

    Tracking positive shifts in visual attention over information-rich query words yields a saliency map that, when used to boost visual and query attention during decoding, reduces object hallucination on CHAIR, POPE, an...

  4. Hallucination of Multimodal Large Language Models: A Survey

    cs.CV 2024-04 accept novelty 5.0 of 10

    The survey organizes causes of hallucinations in MLLMs, reviews evaluation benchmarks and metrics, and outlines mitigation approaches plus open questions.

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