From Weights to Concepts: Data-Free Interpretability of CLIP via Singular Vector Decomposition
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910073345802240 |
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| author | Gentile, Francesco Dall'Asen, Nicola Tonini, Francesco Mancini, Massimiliano Vaquero, Lorenzo Ricci, Elisa |
| author_facet | Gentile, Francesco Dall'Asen, Nicola Tonini, Francesco Mancini, Massimiliano Vaquero, Lorenzo Ricci, Elisa |
| contents | As vision-language models are deployed at scale, understanding their internal mechanisms becomes increasingly critical. Existing interpretability methods predominantly rely on activations, making them dataset-dependent, vulnerable to data bias, and often restricted to coarse head-level explanations. We introduce SITH (Semantic Inspection of Transformer Heads), a fully data-free, training-free framework that directly analyzes CLIP's vision transformer in weight space. For each attention head, we decompose its value-output matrix into singular vectors and interpret each one via COMP (Coherent Orthogonal Matching Pursuit), a new algorithm that explains them as sparse, semantically coherent combinations of human-interpretable concepts. We show that SITH yields coherent, faithful intra-head explanations, validated through reconstruction fidelity and interpretability experiments. This allows us to use SITH for precise, interpretable weight-space model edits that amplify or suppress specific concepts, improving downstream performance without retraining. Furthermore, we use SITH to study model adaptation, showing how fine-tuning primarily reweights a stable semantic basis rather than learning entirely new features. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_24653 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From Weights to Concepts: Data-Free Interpretability of CLIP via Singular Vector Decomposition Gentile, Francesco Dall'Asen, Nicola Tonini, Francesco Mancini, Massimiliano Vaquero, Lorenzo Ricci, Elisa Computer Vision and Pattern Recognition As vision-language models are deployed at scale, understanding their internal mechanisms becomes increasingly critical. Existing interpretability methods predominantly rely on activations, making them dataset-dependent, vulnerable to data bias, and often restricted to coarse head-level explanations. We introduce SITH (Semantic Inspection of Transformer Heads), a fully data-free, training-free framework that directly analyzes CLIP's vision transformer in weight space. For each attention head, we decompose its value-output matrix into singular vectors and interpret each one via COMP (Coherent Orthogonal Matching Pursuit), a new algorithm that explains them as sparse, semantically coherent combinations of human-interpretable concepts. We show that SITH yields coherent, faithful intra-head explanations, validated through reconstruction fidelity and interpretability experiments. This allows us to use SITH for precise, interpretable weight-space model edits that amplify or suppress specific concepts, improving downstream performance without retraining. Furthermore, we use SITH to study model adaptation, showing how fine-tuning primarily reweights a stable semantic basis rather than learning entirely new features. |
| title | From Weights to Concepts: Data-Free Interpretability of CLIP via Singular Vector Decomposition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.24653 |