MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation
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.
670f31c4e218fd7c74db69ad
Chenxi Wang
670f31c4e218fd7c74db69ae
Xiang Chen
670f31c4e218fd7c74db69af
Ningyu Zhang
claimed_verified
2024-10-16T09:43:40.404Z
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/avatars/e0fccbb2577d76088e09f054c35cffbc.svg
Ningyu Zhang
user
Ningyu
670f31c4e218fd7c74db69b0
Bozhong Tian
670f31c4e218fd7c74db69b1
Haoming Xu
670f31c4e218fd7c74db69b2
Shumin Deng
670f31c4e218fd7c74db69b3
Huajun Chen
MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation
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.
A novel dynamic correction decoding method for multimodal large language models reduces hallucination rates by integrating knowledge from preceding layers into the final output.
multimodal large language models hallucination phenomena dynamic correction decoding preceding layers knowledge priors output logits model agnostic classic decoding strategies