Interpretable and Steerable Concept Bottleneck Sparse Autoencoders
This addresses the practical utility of SAEs for interpretability and steering in large vision-language models, but it is incremental as it builds on existing SAE frameworks.
The paper tackled the problem of sparse autoencoders (SAEs) having many neurons with low interpretability or steerability and lacking user-desired concepts, proposing Concept Bottleneck Sparse Autoencoders (CB-SAE) to prune low-utility neurons and add a concept bottleneck, resulting in improvements of +32.1% in interpretability and +14.5% in steerability across LVLMs and image generation tasks.
Sparse autoencoders (SAEs) promise a unified approach for mechanistic interpretability, concept discovery, and model steering in LLMs and LVLMs. However, realizing this potential requires that the learned features be both interpretable and steerable. To that end, we introduce two new computationally inexpensive interpretability and steerability metrics and conduct a systematic analysis on LVLMs. Our analysis uncovers two observations; (i) a majority of SAE neurons exhibit either low interpretability or low steerability or both, rendering them ineffective for downstream use; and (ii) due to the unsupervised nature of SAEs, user-desired concepts are often absent in the learned dictionary, thus limiting their practical utility. To address these limitations, we propose Concept Bottleneck Sparse Autoencoders (CB-SAE) - a novel post-hoc framework that prunes low-utility neurons and augments the latent space with a lightweight concept bottleneck aligned to a user-defined concept set. The resulting CB-SAE improves interpretability by +32.1% and steerability by +14.5% across LVLMs and image generation tasks. We will make our code and model weights available.