The Hidden Life of Tokens: Reducing Hallucination of Large Vision-Language Models Via Visual Information Steering

Zhuowei Li, Haizhou Shi, Yunhe Gao, Di Liu, Zhenting Wang, Yuxiao Chen, Ting Liu, Long Zhao, Hao Wang, Dimitris N. Metaxas
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:35799-35819, 2025.

Abstract

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 about 40% on evaluated open-ended generation task, and it consistently outperforms existing methods on four benchmarks across four architectures under three decoding strategies. Code is available at https://github.com/LzVv123456/VISTA.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-li25ca, title = {The Hidden Life of Tokens: Reducing Hallucination of Large Vision-Language Models Via Visual Information Steering}, author = {Li, Zhuowei and Shi, Haizhou and Gao, Yunhe and Liu, Di and Wang, Zhenting and Chen, Yuxiao and Liu, Ting and Zhao, Long and Wang, Hao and Metaxas, Dimitris N.}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {35799--35819}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/li25ca/li25ca.pdf}, url = {https://proceedings.mlr.press/v267/li25ca.html}, abstract = {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 about 40% on evaluated open-ended generation task, and it consistently outperforms existing methods on four benchmarks across four architectures under three decoding strategies. Code is available at https://github.com/LzVv123456/VISTA.} }
Endnote
%0 Conference Paper %T The Hidden Life of Tokens: Reducing Hallucination of Large Vision-Language Models Via Visual Information Steering %A Zhuowei Li %A Haizhou Shi %A Yunhe Gao %A Di Liu %A Zhenting Wang %A Yuxiao Chen %A Ting Liu %A Long Zhao %A Hao Wang %A Dimitris N. Metaxas %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-li25ca %I PMLR %P 35799--35819 %U https://proceedings.mlr.press/v267/li25ca.html %V 267 %X 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 about 40% on evaluated open-ended generation task, and it consistently outperforms existing methods on four benchmarks across four architectures under three decoding strategies. Code is available at https://github.com/LzVv123456/VISTA.
APA
Li, Z., Shi, H., Gao, Y., Liu, D., Wang, Z., Chen, Y., Liu, T., Zhao, L., Wang, H. & Metaxas, D.N.. (2025). The Hidden Life of Tokens: Reducing Hallucination of Large Vision-Language Models Via Visual Information Steering. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:35799-35819 Available from https://proceedings.mlr.press/v267/li25ca.html.

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