Line of Sight: On Linear Representations in VLLMs
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910989248626688 |
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| author | Rajaram, Achyuta Schwettmann, Sarah Andreas, Jacob Conmy, Arthur |
| author_facet | Rajaram, Achyuta Schwettmann, Sarah Andreas, Jacob Conmy, Arthur |
| contents | Language models can be equipped with multimodal capabilities by fine-tuning on embeddings of visual inputs. But how do such multimodal models represent images in their hidden activations? We explore representations of image concepts within LlaVA-Next, a popular open-source VLLM. We find a diverse set of ImageNet classes represented via linearly decodable features in the residual stream. We show that the features are causal by performing targeted edits on the model output. In order to increase the diversity of the studied linear features, we train multimodal Sparse Autoencoders (SAEs), creating a highly interpretable dictionary of text and image features. We find that although model representations across modalities are quite disjoint, they become increasingly shared in deeper layers. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_04706 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Line of Sight: On Linear Representations in VLLMs Rajaram, Achyuta Schwettmann, Sarah Andreas, Jacob Conmy, Arthur Computer Vision and Pattern Recognition Artificial Intelligence Language models can be equipped with multimodal capabilities by fine-tuning on embeddings of visual inputs. But how do such multimodal models represent images in their hidden activations? We explore representations of image concepts within LlaVA-Next, a popular open-source VLLM. We find a diverse set of ImageNet classes represented via linearly decodable features in the residual stream. We show that the features are causal by performing targeted edits on the model output. In order to increase the diversity of the studied linear features, we train multimodal Sparse Autoencoders (SAEs), creating a highly interpretable dictionary of text and image features. We find that although model representations across modalities are quite disjoint, they become increasingly shared in deeper layers. |
| title | Line of Sight: On Linear Representations in VLLMs |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2506.04706 |