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Towards Interpreting Visual Information Processing in Vision-Language Models

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arxiv 2410.07149 v2 pith:ID67LVCX submitted 2024-10-09 cs.CV cs.LG

Clement Neo , Luke Ong , Philip Torr , Mor Geva , David Krueger , Fazl Barez This is my paper · ORCID
classification cs.CVcs.LG
keywords visualinformationmodelslanguageobjectprocessingrepresentationstoken
verification ladder T0 review T1 audit T2 compute T3 formal
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Vision-Language Models (VLMs) are powerful tools for processing and understanding text and images. We study the processing of visual tokens in the language model component of LLaVA, a prominent VLM. Our approach focuses on analyzing the localization of object information, the evolution of visual token representations across layers, and the mechanism of integrating visual information for predictions. Through ablation studies, we demonstrated that object identification accuracy drops by over 70\% when object-specific tokens are removed. We observed that visual token representations become increasingly interpretable in the vocabulary space across layers, suggesting an alignment with textual tokens corresponding to image content. Finally, we found that the model extracts object information from these refined representations at the last token position for prediction, mirroring the process in text-only language models for factual association tasks. These findings provide crucial insights into how VLMs process and integrate visual information, bridging the gap between our understanding of language and vision models, and paving the way for more interpretable and controllable multimodal systems.

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Cited by 15 Pith papers

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