Quantifying Structure in CLIP Embeddings: A Statistical Framework for Concept Interpretation

• URL: http://arxiv.org/abs/2506.13831v1
• Date: Mon, 16 Jun 2025 02:43:11 GMT
• Title: Quantifying Structure in CLIP Embeddings: A Statistical Framework for Concept Interpretation
• Authors: Jitian Zhao, Chenghui Li, Frederic Sala, Karl Rohe,
• Abstract summary: Concept-based approaches aim to identify human-understandable concepts within a model's internal representations.<n>Current methods lack statistical rigor, making it challenging to validate identified concepts and compare different techniques.<n>We propose a hypothesis testing framework that quantifies rotation-sensitive structures within the CLIP embedding space.<n>Unlike existing approaches, it offers theoretical guarantees that discovered concepts represent robust, reproducible patterns.
• Score: 13.206499575700219
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Concept-based approaches, which aim to identify human-understandable concepts within a model's internal representations, are a promising method for interpreting embeddings from deep neural network models, such as CLIP. While these approaches help explain model behavior, current methods lack statistical rigor, making it challenging to validate identified concepts and compare different techniques. To address this challenge, we introduce a hypothesis testing framework that quantifies rotation-sensitive structures within the CLIP embedding space. Once such structures are identified, we propose a post-hoc concept decomposition method. Unlike existing approaches, it offers theoretical guarantees that discovered concepts represent robust, reproducible patterns (rather than method-specific artifacts) and outperforms other techniques in terms of reconstruction error. Empirically, we demonstrate that our concept-based decomposition algorithm effectively balances reconstruction accuracy with concept interpretability and helps mitigate spurious cues in data. Applied to a popular spurious correlation dataset, our method yields a 22.6% increase in worst-group accuracy after removing spurious background concepts.

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