A new SAE-based framework extracts visual, textual, and multimodal concepts from VLMs and reports up to 45% better visual concept quality on a VQA dataset while identifying multimodal concepts.
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Extraction and Analysis of Multimodal Concepts in Vision Language Models through Sparse Autoencoders
A new SAE-based framework extracts visual, textual, and multimodal concepts from VLMs and reports up to 45% better visual concept quality on a VQA dataset while identifying multimodal concepts.