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V-DPO: Mitigating Hallucination in Large Vision Language Models via Vision-Guided Direct Preference Optimization
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Large vision-language models (LVLMs) suffer from hallucination, resulting in misalignment between the output textual response and the input visual content. Recent research indicates that the over-reliance on the Large Language Model (LLM) backbone, as one cause of the LVLM hallucination, inherently introduces bias from language priors, leading to insufficient context attention to the visual inputs. We tackle this issue of hallucination by mitigating such over-reliance through preference learning. We propose Vision-guided Direct Preference Optimization (V-DPO) to enhance visual context learning at training time. To interpret the effectiveness and generalizability of V-DPO on different types of training data, we construct a synthetic dataset containing both response- and image-contrast preference pairs, compared against existing human-annotated hallucination samples. Our approach achieves significant improvements compared with baseline methods across various hallucination benchmarks. Our analysis indicates that V-DPO excels in learning from image-contrast preference data, demonstrating its superior ability to elicit and understand nuances of visual context. Our code is publicly available at https://github.com/YuxiXie/V-DPO.
Forward citations
Cited by 6 Pith papers
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LPOI: Listwise Preference Optimization for Vision Language Models
LPOI reduces VLM hallucination by training the model to prefer the original image over progressively masked versions of the same image, using a listwise ranking loss built from pairwise preference data.
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Groc-PO: Grounded Context Preference Optimization for Truthful Multimodal LLMs
Groc-PO applies preference optimization at three grounded stages — object grounding, contextual grounding, grounded reasoning — and outperforms final-answer-only DPO on hallucination and complex-reasoning benchmarks.
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Zooming from Context to Cue: Hierarchical Preference Optimization for Multi-Image MLLMs
Context-to-Cue Direct Preference Optimization (CcDPO) reduces multi-image hallucinations in 7B multimodal LLMs by training on perturbed full-sequence captions and region-focused visual prompts, improving average multi...
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LenGuard-GPC: Length Guarding with Guided-Prompt Consistency for Spatial Reasoning Reinforce Learning
LenGuard-GPC adds a token-level KL consistency reward between standard and guided prompts, plus a staged length bonus, to GRPO training of Qwen3-VL-8B and reports better accuracy with shorter responses on multi-view s...
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LeanPO: Lean Preference Optimization for Likelihood Alignment in Video-LLMs
LeanPO improves Video-LLM alignment by using a reference-free average-likelihood reward, self-generated winning/losing pairs, and dynamic label smoothing, yielding gains on six video benchmarks.
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Empowering Multimodal LLMs with External Tools: A Comprehensive Survey
A survey paper maps how external tools are used to augment multimodal large language models across data, tasks, evaluation, and future directions.
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