Concept Bottleneck Models (CBMs) have garnered much attention for their ability to elucidate the prediction process through a human-understandable concept layer. However, most previous studies focused on static scenarios where the data and concepts are assumed to be fixed and clean. In real-world applications, deployed models require continuous maintenance: we often need to remove erroneous or sensitive data (unlearning), correct mislabeled concepts, or incorporate newly acquired samples (incremental learning) to adapt to evolving environments. Thus, deriving efficient editable CBMs without retraining from scratch remains a significant challenge, particularly in large-scale applications. To address these challenges, we propose Controllable Concept Bottleneck Models (CCBMs). Specifically, CCBMs support three granularities of model editing: concept-label-level, concept-level, and data-level, the latter of which encompasses both data removal and data addition. CCBMs enjoy mathematically rigorous closed-form approximations derived from influence functions that obviate the need for retraining. Experimental results demonstrate the efficiency and adaptability of our CCBMs, affirming their practical value in enabling dynamic and trustworthy CBMs.
@article{arxiv.2601.00451,
title = {Controllable Concept Bottleneck Models},
author = {Hongbin Lin and Chenyang Ren and Juangui Xu and Zhengyu Hu and Cheng-Long Wang and Yao Shu and Hui Xiong and Jingfeng Zhang and Di Wang and Lijie Hu},
journal= {arXiv preprint arXiv:2601.00451},
year = {2026}
}
Comments
arXiv admin note: substantial text overlap with arXiv:2405.15476