The Hidden Life of Tokens: Reducing Hallucination of Large
  Vision-Language Models via Visual Information Steering
Large Vision-Language Models (LVLMs) can reason effectively over both textual
and visual inputs, but they tend to hallucinate syntactically coherent yet
visually ungrounded contents. In this paper, we investigate the internal
dynamics of hallucination by examining the tokens logits rankings throughout
the generation process, revealing three key patterns in how LVLMs process
information: (1) gradual visual information loss -- visually grounded tokens
gradually become less favored throughout generation, and (2) early excitation
-- semantically meaningful tokens achieve peak activation in the layers earlier
than the final layer. (3) hidden genuine information -- visually grounded
tokens though not being eventually decided still retain relatively high
rankings at inference. Based on these insights, we propose VISTA (Visual
Information Steering with Token-logit Augmentation), a training-free
inference-time intervention framework that reduces hallucination while
promoting genuine information. VISTA works by combining two complementary
approaches: reinforcing visual information in activation space and leveraging
early layer activations to promote semantically meaningful decoding. Compared
to existing methods, VISTA requires no external supervision and is applicable
to various decoding strategies. Extensive experiments show that VISTA on
average reduces hallucination by abount 40% on evaluated open-ended generation
task, and it consistently outperforms existing methods on four benchmarks
across four architectures under three decoding strategies.
67aab82e6024056209d727a8
Zhuowei Li
67aab82e6024056209d727a9
Haizhou Shi
67aab82e6024056209d727aa
Yunhe Gao
67aab82e6024056209d727ab
Di Liu
67aab82e6024056209d727ac
Zhenting Wang
67aab82e6024056209d727ad
Yuxiao Chen
67aab82e6024056209d727ae
Ting Liu
67aab82e6024056209d727af
Long Zhao
claimed_verified
2025-02-18T09:34:47.676Z
650c249887dcda6616baa040
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/tIVfxoAHAJ0sWyDMkaarA.jpeg
Long Zhao
user
garyzhao9012
67aab82e6024056209d727b0
Hao Wang
67aab82e6024056209d727b1
Dimitris N. Metaxas
The Hidden Life of Tokens: Reducing Hallucination of Large
  Vision-Language Models via Visual Information Steering
Large Vision-Language Models (LVLMs) can reason effectively over both textual
and visual inputs, but they tend to hallucinate syntactically coherent yet
visually ungrounded contents. In this paper, we investigate the internal
dynamics of hallucination by examining the tokens logits rankings throughout
the generation process, revealing three key patterns in how LVLMs process
information: (1) gradual visual information loss -- visually grounded tokens
gradually become less favored throughout generation, and (2) early excitation
-- semantically meaningful tokens achieve peak activation in the layers earlier
than the final layer. (3) hidden genuine information -- visually grounded
tokens though not being eventually decided still retain relatively high
rankings at inference. Based on these insights, we propose VISTA (Visual
Information Steering with Token-logit Augmentation), a training-free
inference-time intervention framework that reduces hallucination while
promoting genuine information. VISTA works by combining two complementary
approaches: reinforcing visual information in activation space and leveraging
early layer activations to promote semantically meaningful decoding. Compared
to existing methods, VISTA requires no external supervision and is applicable
to various decoding strategies. Extensive experiments show that VISTA on
average reduces hallucination by abount 40% on evaluated open-ended generation
task, and it consistently outperforms existing methods on four benchmarks
across four architectures under three decoding strategies.
VISTA reduces hallucination in large vision-language models by steering visual information during inference without additional training.
LVLMs hallucination tokens logits rankings visual information loss early excitation hidden genuine information VISTA activation space semantically meaningful decoding decoding strategies open-ended generation tasks benchmarks architectures