Sparse Autoencoders Learn Monosemantic Features in Vision-Language Models

• URL: http://arxiv.org/abs/2504.02821v1
• Date: Thu, 03 Apr 2025 17:58:35 GMT
• Title: Sparse Autoencoders Learn Monosemantic Features in Vision-Language Models
• Authors: Mateusz Pach, Shyamgopal Karthik, Quentin Bouniot, Serge Belongie, Zeynep Akata,
• Abstract summary: Sparse Autoencoders (SAEs) have been shown to enhance interpretability and steerability in Large Language Models (LLMs)<n>In this work, we extend the application of SAEs to Vision-Language Models (VLMs), such as CLIP, and introduce a comprehensive framework for evaluating monosemanticity in vision representations.
• Score: 50.587868616659826
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sparse Autoencoders (SAEs) have recently been shown to enhance interpretability and steerability in Large Language Models (LLMs). In this work, we extend the application of SAEs to Vision-Language Models (VLMs), such as CLIP, and introduce a comprehensive framework for evaluating monosemanticity in vision representations. Our experimental results reveal that SAEs trained on VLMs significantly enhance the monosemanticity of individual neurons while also exhibiting hierarchical representations that align well with expert-defined structures (e.g., iNaturalist taxonomy). Most notably, we demonstrate that applying SAEs to intervene on a CLIP vision encoder, directly steer output from multimodal LLMs (e.g., LLaVA) without any modifications to the underlying model. These findings emphasize the practicality and efficacy of SAEs as an unsupervised approach for enhancing both the interpretability and control of VLMs.

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