MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation
Authors: name: Chenxi Wang, name: Xiang Chen, name: Ningyu Zhang, name: Bozhong Tian, name: Haoming Xu, name: Shumin Deng, name: Huajun Chen

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.