🤖 AI Summary
This work addresses the issue that latent features in sparse autoencoders (SAEs) often conflate multiple semantic subspaces, compromising atomicity and interpretability. To mitigate this, the authors propose a joint training approach that introduces a small meta-SAE to sparsely reconstruct the columns of the main SAE’s decoder. They further impose a decomposability penalty on directions that are easily reconstructible by the meta dictionary, directly optimizing feature atomicity during training. This method is the first to explicitly treat decomposability as a training objective, effectively reducing cross-semantic aliasing. Experiments show a 7.5% reduction in average |φ| and a 7.6% improvement in automatic interpretability scores on GPT-2 Large, along with an 8.6% gain in Fuzz scores on Gemma 2 9B. Qualitative analysis confirms that polysemantic features are successfully disentangled into semantically coherent atomic subfeatures.
📝 Abstract
Sparse autoencoders (SAEs) are increasingly used for safety-relevant applications including alignment detection and model steering. These use cases require SAE latents to be as atomic as possible. Each latent should represent a single coherent concept drawn from a single underlying representational subspace. In practice, SAE latents blend representational subspaces together. A single feature can activate across semantically distinct contexts that share no true common representation, muddying an already complex picture of model computation. We introduce a joint training objective that directly penalizes this subspace blending. A small meta SAE is trained alongside the primary SAE to sparsely reconstruct the primary SAE's decoder columns; the primary SAE is penalized whenever its decoder directions are easy to reconstruct from the meta dictionary. This occurs whenever latent directions lie in a subspace spanned by other primary directions. This creates gradient pressure toward more mutually independent decoder directions that resist sparse meta-compression.
On GPT-2 large (layer 20), the selected configuration reduces mean $|\varphi|$ by 7.5% relative to an identical solo SAE trained on the same data. Automated interpretability (fuzzing) scores improve by 7.6%, providing external validation of the atomicity gain independent of the training and co-occurrence metrics. Reconstruction overhead is modest. Results on Gemma 2 9B are directional. On not-fully-converged SAEs, the same parameterization yields the best results, a $+8.6\%$ $Δ$Fuzz. Though directional, this is an encouraging sign that the method transfers to a larger model. Qualitative analysis confirms that features firing on polysemantic tokens are split into semantically distinct sub-features, each specializing in a distinct representational subspace.