Efficient Training of Sparse Autoencoders for Large Language Models via Layer Clustering
Keywords: Sparse Autoencoders (SAEs), Meta Learning, Mechanistic Interpretability, Large Language Models (LLMs)
Abstract: Sparse Autoencoders (SAEs) have recently been employed as an unsupervised approach for understanding the inner workings of Large Language Models (LLMs). They reconstruct the model’s activations with a sparse linear combination of interpretable features. However, training SAEs is computationally intensive, especially as models grow in size and complexity. To address this challenge, we propose a novel training strategy that reduces the number of trained SAEs from one per layer to one for a given group of contiguous layers. Our experimental results on Pythia 160M highlight a 3x speedup without compromising the reconstruction quality and performance on downstream tasks. Therefore, layer clustering presents an efficient approach to train SAEs in modern LLMs.
Primary Area: interpretability and explainable AI
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Submission Number: 14058
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