MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation
This addresses hallucination issues in MLLMs, which is a critical problem for improving reliability in multimodal AI applications, though it is an incremental method building on existing decoding strategies.
The paper tackles the problem of hallucination in Multimodal Large Language Models (MLLMs) by proposing a dynamic correction decoding method (DeCo) that integrates visual information from preceding layers to adjust outputs, resulting in a large reduction in hallucination rates on benchmarks.
Multimodal Large Language Models (MLLMs) frequently exhibit hallucination phenomena, but the underlying reasons remain poorly understood. In this paper, we present an empirical analysis and find that, although MLLMs incorrectly generate the objects in the final output, they are actually able to recognize visual objects in the preceding layers. We speculate that this may be due to the strong knowledge priors of the language model suppressing the visual information, leading to hallucinations. Motivated by this, we propose a novel dynamic correction decoding method for MLLMs DeCo, which adaptively selects the appropriate preceding layers and proportionally integrates knowledge into the final layer to adjust the output logits. Note that DeCo is model agnostic and can be seamlessly incorporated with various classic decoding strategies and applied to different MLLMs. We evaluate DeCo on widely-used benchmarks, demonstrating that it can reduce hallucination rates by a large margin compared to baselines, highlighting its potential to mitigate hallucinations. Code is available at https://github.com/zjunlp/DeCo.