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arXiv · 2410.08869

Evolution of SAE Features Across Layers in LLMs

Daniel Balcells·Benjamin Lerner·Michael Oesterle·Ediz Ucar·Stefan Heimersheim
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Abstract

Sparse Autoencoders for transformer-based language models are typically defined independently per layer. In this work we analyze statistical relationships between features in adjacent layers to understand how features evolve through a forward pass. We provide a graph visualization interface for features and their most similar next-layer neighbors (https://stefanhex.com/spar-2024/feature-browser/), and build communities of related features across layers. We find that a considerable amount of features are passed through from a previous layer, some features can be expressed as quasi-boolean combinations of previous features, and some features become more specialized in later layers.

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Daniel Balcells, Benjamin Lerner, Michael Oesterle, Ediz Ucar, Stefan Heimersheim. 2024-10-11. Evolution of SAE Features Across Layers in LLMs. https://arxiv.org/abs/2410.08869

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