DAMO: Decoding by Accumulating Activations Momentum for Mitigating Hallucinations in Vision-Language Models

Kaishen Wang, Hengrui Gu, Meijun Gao, Kaixiong Zhou

Keywords: Vision-Language Models (VLMs), Hallucinations, Decoding Method, Momentum Techniques

TL;DR: To address the hallucination problem in Vision-Language Models (VLMs), we propose a novel decoding method inspired by momentum techniques.

Abstract: Large Vision-Language Models (VLMs) exhibit significant potential in multimodal tasks but often struggle with hallucinations—responses that are plausible yet visually ungrounded. In this work, we investigate the layer-wise prediction tendencies of VLMs and conduct an in-depth analysis of their decoding mechanism. We observe that VLMs tend to ``overthink'' during the final stages of decoding, making significant prediction shifts in the last few layers often favoring incorrect results, which leads to a surge in hallucinative outputs. Leveraging this localized pattern, we propose a novel decoding strategy inspired by the momentum analogy used in gradient descent-based optimizers. Our method enforces decoding consistency across layers in an adaptive manner during forward passes—an under-explored approach in existing works. This strategy significantly improves the reliability and performance of VLMs in various multimodal tasks, while introducing only negligible efficiency overhead.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 11551