aiqbench analysis
This paper is squarely about AI: a reinforcement learning method for reducing object hallucination in vision-language models used for image captioning. The primary subject is machine learning research, so it falls within the site's scope. The reported reductions (up to 41% on COCO, 29% on Visual Genome) are results on a single baseline (InstructBLIP) and should be read as benchmark gains rather than general proof of reliability. The approach's main appeal for practitioners is replacing costly human feedback with automated NLP metrics, though the authors themselves flag reward hacking and sparse-feedback risks that still need scrutiny.
Metric-Driven Reinforcement Learning for Object Hallucination Reduction in LVLM-Based Image Captioning
arXiv:2610.14976v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have excelled in joint visual and language understanding, particularly in image captioning. However, LVLMs remain prone to object hallucination, describing non-existent entities in the given image. Previous remedies, including dataset-enhanced supervised training and reinforcement learning from human feedback, show promise but rely heavily on costly human involvement, limiting scalability. To address this, we propose a reinforcement learning framework to mitigate object hallucination in LVLM-based image captioning, driven entirely by natural language processing (NLP) metrics. While optimizing reinforcement learning solely on NLP metrics offers scalability, it also introduces pitfalls: reward hacking, sparse feedback, and costly training. Our framework sidesteps these by introducing an Object Consistency Score (OCS) capturing both correctness and coverage of objects, incorporating KL-regularization for denser token-level guidance, and designing a lightweight PPO variant that fine-tunes adapters atop a frozen LVLM backbone, achieving efficiency and stability. Extensive experiments on the InstructBLIP baseline demonstrate the effectiveness and generalizability of our framework, achieving a substantial hallucination reduction of up to 41\% on the COCO dataset and 29\% on the Visual Genome. Furthermore, the framework can enhance caption quality by incorporating complementary evaluation metrics such as BERTScore and METEOR. These results reveal a broader insight in multimodal learning: LVLMs can effectively self-correct hallucinations using metric feedback, motivating deeper exploration into scalable reinforcement frameworks that significantly reduce human effort.
Abstract:Large Vision-Language Models (LVLMs) have excelled in joint visual and language understanding, particularly in image captioning. However, LVLMs remain prone to object hallucination, describing non-existent entities in the given image. Previous remedies, including dataset-enhanced supervised training and reinforcement learning from human feedback, show promise but rely heavily on costly human involvement, limiting scalability. To address this, we propose a reinforcement learning framework to mitigate object hallucination in LVLM-based image captioning, driven entirely by natural language processing (NLP) metrics. While optimizing reinforcement learning solely on NLP metrics offers scalability, it also introduces pitfalls: reward hacking, sparse feedback, and costly training. Our framework sidesteps these by introducing an Object Consistency Score (OCS) capturing both correctness and coverage of objects, incorporating KL-regularization for denser token-level guidance, and designing a lightweight PPO variant that fine-tunes adapters atop a frozen LVLM backbone, achieving efficiency and stability. Extensive experiments on the InstructBLIP baseline demonstrate the effectiveness and generalizability of our framework, achieving a substantial hallucination reduction of up to 41\% on the COCO dataset and 29\% on the Visual Genome. Furthermore, the framework can enhance caption quality by incorporating complementary evaluation metrics such as BERTScore and METEOR. These results reveal a broader insight in multimodal learning: LVLMs can effectively self-correct hallucinations using metric feedback, motivating deeper exploration into scalable reinforcement frameworks that significantly reduce human effort.
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
arXiv:2610.14976 [cs.CV]
(or arXiv:2610.14976v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.14976 arXiv-issued DOI via DataCite (pending registration)
T. Nguyen, H. S. Yoon, T. M. Luu, E. Yoon and C. D. Yoo, "Metric-Driven Reinforcement Learning for Object Hallucination Reduction in LVLM-Based Image Captioning," in IEEE Access, vol. 14, pp. 125177-125195, 2026
https://doi.org/10.1109/ACCESS.2026.3723028 DOI(s) linking to related resources
Full text under the publisher's open licence · 283 words
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