mlsanityData to 14 Oct 2026

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models

arXiv 2505.24649 ↗First posted 30 May 2025cs.CV cs.AIcited by 1

On the map: Multimodal LLMs / vision-language models → Hallucination mitigation in LVLMs

Abstract (arXiv)

Large vision-language models have become widely adopted to advance in various domains. However, developing a trustworthy system with minimal interpretable characteristics of large-scale models presents a significant challenge. One of the most prevalent terms associated with the fallacy functions caused by these systems is hallucination, where the language model generates a response that does not correspond to the visual content. To mitigate this problem, several approaches have been developed, and one prominent direction is to ameliorate the decoding process. In this paper, we propose a new Bijective Maximum Likelihood Learning (BIMA) approach to hallucination mitigation using normalizing flow theories. The proposed BIMA method can efficiently mitigate the hallucination problem in prevailing vision-language models, resulting in significant improvements. Notably, BIMA achieves the average F1 score of 85.06% on POPE benchmark and remarkably reduce CHAIRS and CHAIRI by 7.6% and 2.6%, respectively. To the best of our knowledge, this is one of the first studies that contemplates the bijection means to reduce hallucination induced by large vision-language models.

Key facts. Cited by 1 paper 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 46 of its 50 references that are in the map. First posted on arXiv 30 May 2025.

Builds on

46 of 50 references.

Closest ancestors · 10

Same direction, newest first

  1. 2025Octopus: Alleviating Hallucination via Dynamic Contrastive Decoding1 Mar
  2. 2024Interpreting and Editing Vision-Language Representations to Mitigate Hallucinations3 Oct
  3. 2024Self-Introspective Decoding: Alleviating Hallucinations for Large Vision-Language Models4 Aug
  4. 2024Paying More Attention to Image: A Training-Free Method for Alleviating Hallucination in LVLMs31 Jul
  5. 2024Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding27 Mar
  6. 2024A Survey on Hallucination in Large Vision-Language Models1 Feb
  7. 2023OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation29 Nov
  8. 2023Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding28 Nov
  9. 2023Detecting and Preventing Hallucinations in Large Vision Language Models11 Aug
  10. 2023Evaluating Object Hallucination in Large Vision-Language Models17 May

Other references · 36

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24 more
  1. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,322
  2. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,234
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026φ-DPO: Fairness Direct Preference Optimization Approach to Continual Learning in Large Multimodal Modelscited by 2

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  1. 2024IBD: Alleviating Hallucinations in Large Vision-Language Models via Image-Biased Decoding
  2. 2025ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models
  3. 2026Test-Time Hallucination Control in Large Vision-Language Models
  4. 2024Hallucination in Multimodal Foundation Models: A Survey on Causes, Corrections, and Evaluations
  5. 2024RITUAL: Random Image Transformations as a Universal Anti-hallucination Lever in Large Vision Language Models
  6. 2025Mixture of Decoding: An Attention-Inspired Adaptive Decoding Strategy to Mitigate Hallucinations in Large Vision-Language Models
  7. 2024Reducing Hallucinations in Vision-Language Models via Latent Space Steering
  8. 2026Hallucination-aware intermediate representation edit in large vision-language models