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arXiv · 2505.12343

Mitigating Hallucinations via Inter-Layer Consistency Aggregation in Large Vision-Language Models

Kai Tang·Jinhao You·Yichen Guo·Yiding Sun·Dongxu Zhang·Wenya Wang·Hanze Li·Tao Luo·Renyuan Li·Xiande Huang·Shanghang Zhang
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Abstract

Despite the impressive capabilities of Large Vision-Language Models (LVLMs), they remain susceptible to hallucinations, where generated content is inconsistent with the input image. Existing training-free hallucination mitigation methods often suffer from unstable performance and high sensitivity to hyperparameter settings, which limits their practicality and broader adoption. In this paper, we propose Decoding with Inter-layer Consistency via Layer Aggregation (DCLA), a training-free decoding mechanism that requires no retraining, fine-tuning, or access to external knowledge bases. Specifically, DCLA constructs a dynamic semantic reference by aggregating representations from previous layers and uses it to correct semantically deviated layers, thereby enforcing inter-layer consistency. Experiments across seven LVLMs and multiple benchmarks demonstrate the generality of DCLA: it surpasses standard decoding by 28.58 MME points on LLaVA1.5-7B and 42.6 MME points on Qwen2.5-VL, while improving POPE accuracy by 2.74 percentage points in the strongest setting.

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Kai Tang, Jinhao You, Yichen Guo, Yiding Sun, Dongxu Zhang, Wenya Wang, Hanze Li, Tao Luo, Renyuan Li, Xiande Huang, Shanghang Zhang. 2025-05-18. Mitigating Hallucinations via Inter-Layer Consistency Aggregation in Large Vision-Language Models. https://arxiv.org/abs/2505.12343

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