给预训练模型加个轻量级模块，让概念间关系更真实，干预更准。
Post-hoc Stochastic Concept Bottleneck Models
- 用小模块在不重训的前提下，为概念添加多变量正态分布关系
- 实测在概念和目标预测上均优于标准CBM，干预时提升明显
- 适合资源受限场景，比从头训练高效得多
概念瓶颈模型（CBMs）通过人类可理解的概念预测目标变量，支持用户干预错误概念以调整输出。尽管建模概念间依赖能提升性能，尤其在干预场景下，但现有方法通常需重训练整个模型，可能因无法访问原始数据或计算资源而不可行。本文提出后验随机概念瓶颈模型（PSCBMs），一种轻量级方法：仅通过添加一个小型协方差预测模块，在不重训主干模型的前提下，为任意预训练的CBM引入概念上的多变量正态分布。我们提出两种训练策略，实验证明在真实数据上，PSCBMs在测试时始终达到或超越标准CBMs在概念与目标准确率上的表现。更重要的是，由于对概念依赖的建模，PSCBMs在干预下的表现远超传统CBMs，同时显著优于从头训练类似随机模型的效率。
原文摘要 · Abstract (English)
Concept Bottleneck Models (CBMs) are interpretable models that predict the target variable through high-level human-understandable concepts, allowing users to intervene on mispredicted concepts to adjust the final output. While recent work has shown that modeling dependencies between concepts can improve CBM performance, especially under interventions, such approaches typically require retraining the entire model, which may be infeasible when access to the original data or compute is limited. In this paper, we introduce Post-hoc Stochastic Concept Bottleneck Models (PSCBMs), a lightweight method that augments any pre-trained CBM with a multivariate normal distribution over concepts by adding only a small covariance-prediction module, without retraining the backbone model. We propose two training strategies and show on real-world data that PSCBMs consistently match or improve both concept and target accuracy over standard CBMs at test time. Furthermore, we show that due to the modeling of concept dependencies, PSCBMs perform much better than CBMs under interventions, while remaining far more efficient than retraining a similar stochastic model from scratch.
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