Residual Decoding: Mitigating Hallucinations in Large Vision-Language Models via History-Aware Residual Guidance

• URL: http://arxiv.org/abs/2602.01047v2
• Date: Tue, 10 Feb 2026 16:46:48 GMT
• Title: Residual Decoding: Mitigating Hallucinations in Large Vision-Language Models via History-Aware Residual Guidance
• Authors: Xinrong Chen, Xu Chu, Yingmin Qiu, Hengyuan Zhang, Jing Xiong, Shiyu Tang, Shuai Liu, Shaokang Yang, Cheng Yang, Hayden Kwok-Hay So, Ngai Wong,
• Abstract summary: Large Vision-Language Models (LVLMs) can reason effectively from image-text inputs and perform well in various multimodal tasks.<n>They are affected by language priors and often produce hallucinations.<n>We propose Residual Decoding (ResDec) to address this problem.
• Score: 31.7541034166056
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Vision-Language Models (LVLMs) can reason effectively from image-text inputs and perform well in various multimodal tasks. Despite this success, they are affected by language priors and often produce hallucinations. Hallucinations denote generated content that is grammatically and syntactically coherent, yet bears no match or direct relevance to actual visual input. To address this problem, we propose Residual Decoding (ResDec). It is a novel training-free method that uses historical information to aid decoding. The method relies on the internal implicit reasoning mechanism and token logits evolution mechanism of LVLMs to correct biases. Extensive experiments demonstrate that ResDec effectively suppresses hallucinations induced by language priors, significantly improves visual grounding, and reduces object hallucinations. In addition to mitigating hallucinations, ResDec also performs exceptionally well on comprehensive LVLM benchmarks, highlighting its broad applicability.

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