Towards Compositionality in Concept Learning

• URL: http://arxiv.org/abs/2406.18534v1
• Date: Wed, 26 Jun 2024 17:59:30 GMT
• Title: Towards Compositionality in Concept Learning
• Authors: Adam Stein, Aaditya Naik, Yinjun Wu, Mayur Naik, Eric Wong,
• Abstract summary: We show that existing unsupervised concept extraction methods find concepts which are not compositional. We propose Compositional Concept Extraction (CCE) for finding concepts which obey these properties. CCE finds more compositional concept representations than baselines and yields better accuracy on four downstream classification tasks.
• Score: 20.960438848942445
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Concept-based interpretability methods offer a lens into the internals of foundation models by decomposing their embeddings into high-level concepts. These concept representations are most useful when they are compositional, meaning that the individual concepts compose to explain the full sample. We show that existing unsupervised concept extraction methods find concepts which are not compositional. To automatically discover compositional concept representations, we identify two salient properties of such representations, and propose Compositional Concept Extraction (CCE) for finding concepts which obey these properties. We evaluate CCE on five different datasets over image and text data. Our evaluation shows that CCE finds more compositional concept representations than baselines and yields better accuracy on four downstream classification tasks. Code and data are available at https://github.com/adaminsky/compositional_concepts .

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