Context-Aware Decoding for Faithful Vision-Language Generation

• URL: http://arxiv.org/abs/2601.05939v1
• Date: Fri, 09 Jan 2026 16:50:57 GMT
• Title: Context-Aware Decoding for Faithful Vision-Language Generation
• Authors: Mehrdad Fazli, Bowen Wei, Ziwei Zhu,
• Abstract summary: Hallucinations, generating responses inconsistent with the visual input, remain a critical limitation of large vision-language models (LVLMs)<n>We probe the layer-wise generation dynamics that drive hallucinations and propose a training-free mitigation strategy.
• Score: 5.258492912374723
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hallucinations, generating responses inconsistent with the visual input, remain a critical limitation of large vision-language models (LVLMs), especially in open-ended tasks such as image captioning and visual reasoning. In this work, we probe the layer-wise generation dynamics that drive hallucinations and propose a training-free mitigation strategy. Employing the Logit Lens, we examine how LVLMs construct next-token distributions across decoder layers, uncovering a pronounced commitment-depth gap: truthful tokens accumulate probability mass on their final candidates earlier than hallucinatory ones. Drawing on this discovery, we introduce Context Embedding Injection (CEI), a lightweight method that harnesses the hidden state of the last input token-the context embedding-as a grounding signal to maintain visual fidelity throughout decoding and curb hallucinations. Evaluated on the CHAIR, AMBER, and MMHal-Bench benchmarks (with a maximum token length of 512), CEI outperforms state-of-the-art baselines across three LVLMs, with its dynamic variant yielding the lowest overall hallucination rates. By integrating novel mechanistic insights with a scalable intervention, this work advances the mitigation of hallucinations in LVLMs.

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