Selective Concept Bottleneck Models Without Predefined Concepts
Keywords: interpretability, concept bottleneck models, concepts
TL;DR: We present a method to convert black-box models into concept bottleneck models without predefined concepts and introduce an input-dependent concept selection mechanism that only retains a sparse set of concepts per input.
Abstract: Concept-based models like Concept Bottleneck Models (CBMs) have garnered significant interest for improving model interpretability by first predicting human-understandable concepts before mapping them to the output classes. Early approaches required costly concept annotations. To alleviate such, recent methods utilized large language models to automatically generate class-specific concept descriptions and learned mappings from a pretrained black-box model’s raw features to these concepts using vision-language models. However, these approaches assume prior knowledge of which concepts the black-box model has learned. In this work, we discover the concepts encoded by the model through unsupervised concept discovery techniques instead. We further propose an input-dependent concept selection mechanism that dynamically retains a sparse set of relevant concepts for each input, enhancing both sparsity and interpretability. Our approach not only improves downstream performance but also needs significantly fewer concepts for accurate classification. Lastly, we show how large vision-language models can guide the editing of our models' weights to correct errors.
Primary Area: interpretability and explainable AI
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Submission Number: 1081
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