Partially Shared Concept Bottleneck Models

• URL: http://arxiv.org/abs/2511.22170v1
• Date: Thu, 27 Nov 2025 07:15:15 GMT
• Title: Partially Shared Concept Bottleneck Models
• Authors: Delong Zhao, Qiang Huang, Di Yan, Yiqun Sun, Jun Yu,
• Abstract summary: Concept Bottleneck Models (CBMs) enhance interpretability by introducing a layer of human-understandable concepts between inputs and predictions.<n>Recent methods automate concept generation using Large Language Models (LLMs) and Vision-Language Models (VLMs)<n>We introduce PS-CBM, a Partially Shared CBM framework that addresses these limitations through three core components.
• Score: 15.871749983667229
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Concept Bottleneck Models (CBMs) enhance interpretability by introducing a layer of human-understandable concepts between inputs and predictions. While recent methods automate concept generation using Large Language Models (LLMs) and Vision-Language Models (VLMs), they still face three fundamental challenges: poor visual grounding, concept redundancy, and the absence of principled metrics to balance predictive accuracy and concept compactness. We introduce PS-CBM, a Partially Shared CBM framework that addresses these limitations through three core components: (1) a multimodal concept generator that integrates LLM-derived semantics with exemplar-based visual cues; (2) a Partially Shared Concept Strategy that merges concepts based on activation patterns to balance specificity and compactness; and (3) Concept-Efficient Accuracy (CEA), a post-hoc metric that jointly captures both predictive accuracy and concept compactness. Extensive experiments on eleven diverse datasets show that PS-CBM consistently outperforms state-of-the-art CBMs, improving classification accuracy by 1.0%-7.4% and CEA by 2.0%-9.5%, while requiring significantly fewer concepts. These results underscore PS-CBM's effectiveness in achieving both high accuracy and strong interpretability.

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