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Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback

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arxiv 2404.14233 v2 pith:TY5A5ROP submitted 2024-04-22 cs.CV cs.AIcs.CLcs.LG

Wenyi Xiao , Ziwei Huang , Leilei Gan , Wanggui He , Haoyuan Li , Zhelun Yu , Fangxun Shu , Hao Jiang
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Linchao Zhu
This is my paper
classification cs.CVcs.AIcs.CLcs.LG
keywords hallucinationlvlmsmitigatingmodelshallucinationspreferenceproposeannotation
verification ladder T0 review T1 audit T2 compute T3 formal
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The rapidly developing Large Vision Language Models (LVLMs) have shown notable capabilities on a range of multi-modal tasks, but still face the hallucination phenomena where the generated texts do not align with the given contexts, significantly restricting the usages of LVLMs. Most previous work detects and mitigates hallucination at the coarse-grained level or requires expensive annotation (e.g., labeling by proprietary models or human experts). To address these issues, we propose detecting and mitigating hallucinations in LVLMs via fine-grained AI feedback. The basic idea is that we generate a small-size sentence-level hallucination annotation dataset by proprietary models, whereby we train a hallucination detection model which can perform sentence-level hallucination detection, covering primary hallucination types (i.e., object, attribute, and relationship). Then, we propose a detect-then-rewrite pipeline to automatically construct preference dataset for training hallucination mitigating model. Furthermore, we propose differentiating the severity of hallucinations, and introducing a Hallucination Severity-Aware Direct Preference Optimization (HSA-DPO) for mitigating hallucination in LVLMs by incorporating the severity of hallucinations into preference learning. Extensive experiments demonstrate the effectiveness of our method.

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Cited by 8 Pith papers

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

  1. T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts

    cs.CV 2024-12 unverdicted novelty 7.0 of 10

    T2I-FactualBench is a new three-tier benchmark for factuality of knowledge-intensive concepts in T2I models, using multi-round VQA evaluation to show SOTA models need improvement.

  2. Detecting and Evaluating Medical Hallucinations in Large Vision Language Models

    cs.CV 2024-06 unverdicted novelty 7.0 of 10

    Presents Med-HallMark benchmark, MediHall Score metric, and MediHallDetector model for hallucination detection and evaluation in medical LVLMs.

  3. Groc-PO: Grounded Context Preference Optimization for Truthful Multimodal LLMs

    cs.CV 2026-07 conditional novelty 6.0 of 10

    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.

  4. Spectral Query-Key Product Weight Steering for Training-Free VLM Hallucination Mitigation

    cs.CV 2026-06 unverdicted novelty 6.0 of 10

    QK Product Steering suppresses dominant singular modes in the per-head QK product of selected middle layers via a closed-form query-only update, yielding 4.0% average relative CHAIR_s reduction on three GQA VLMs.

  5. Vocabulary Hijacking in LVLMs: Unveiling Critical Attention Heads by Excluding Inert Tokens to Mitigate Hallucination

    cs.MM 2026-05 unverdicted novelty 6.0 of 10

    LVLMs show vocabulary hijacking by inert tokens that decode to hijacking anchors; HABI locates them, NHAR finds resilient heads, and HAVAE boosts those heads to cut hallucinations.

  6. Mitigating Object Hallucinations via Sentence-Level Early Intervention

    cs.CV 2025-07 conditional novelty 6.0 of 10

    SENTINEL reduces MLLM object hallucinations by over 90% via sentence-level early intervention with detector-bootstrapped preference data and C-DPO loss, outperforming prior SOTA on hallucination and capability benchmarks.

  7. 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.

  8. Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning

    cs.CL 2025-02 unverdicted novelty 2.0 of 10

    Position paper claims multimodal LLMs can significantly advance scientific reasoning and proposes a four-stage roadmap plus challenges and suggestions.

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