A central goal of interpretability is to recover representations of causally relevant concepts from the activations of neural networks. The quality of these concept representations is typically evaluated in isolation, and under implicit independence assumptions that may not hold in practice. Thus, it is unclear whether common featurization methods - including sparse autoencoders (SAEs) and sparse probes - recover disentangled representations of these concepts. This study proposes a multi-concept evaluation setting where we control the correlations between textual concepts, such as sentiment, domain, and tense, and analyze performance under increasing correlations between them. We first evaluate the extent to which featurizers can learn disentangled representations of each concept under increasing correlational strengths. We observe a one-to-many relationship from concepts to features: features correspond to no more than one concept, but concepts are distributed across many features. Then, we perform steering experiments, measuring whether each concept is independently manipulable. Even when trained on uniform distributions of concepts, SAE features generally affect many concepts when steered, indicating that they are neither selective nor independent; nonetheless, features affect disjoint subspaces. These results suggest that correlational metrics for measuring disentanglement are generally not sufficient for establishing independence when steering, and that affecting disjoint subspaces is not sufficient for concept selectivity. These results underscore the importance of compositional evaluations in interpretability research.

翻译：可解释性的核心目标是从神经网络激活中恢复因果相关概念的表征。这些概念表征的质量通常被孤立地评估，并基于实践中可能不成立的隐式独立性假设。因此，常见特征化方法——包括稀疏自编码器（SAEs）和稀疏探针——是否能够恢复这些概念的解耦表征尚不明确。本研究提出一个多概念评估框架，通过控制文本概念（如情感、领域和时态）之间的相关性，分析其在相关性增强下的性能。我们首先评估特征化方法在相关性强度递增时学习每个概念解耦表征的能力。观察到概念与特征之间存在一对多关系：特征至多对应一个概念，但概念分布于多个特征中。随后，我们进行操控实验，测量每个概念是否可独立操纵。即使在概念均匀分布上训练，SAE特征在操控时通常影响多个概念，表明其既不具备选择性也不独立；尽管如此，这些特征影响互不相交的子空间。这些结果表明，用于衡量解耦的相关性度量通常不足以在操控时确立独立性，且影响不相交子空间并不等同于概念选择性。这些发现强调了可解释性研究中组合式评估的重要性。