AI RESEARCH
Learning Concept Bottleneck Models from Mechanistic Explanations
arXiv CS.LG
•
ArXi:2603.07343v1 Announce Type: new Concept Bottleneck Models (CBMs) aim for ante-hoc interpretability by learning a bottleneck layer that predicts interpretable concepts before the decision. State-of-the-art approaches typically select which concepts to learn via human specification, open knowledge graphs, prompting an LLM, or using general CLIP concepts. However, concepts defined a-priori may not have sufficient predictive power for the task or even be learnable from the available data.