A Testable Theory of Atomic Features

Abstract:We develop and test a theory of language model representations in which there exist atomic features. Our main theoretical insight is that in such a model, sparse dictionaries (e.g., SAEs) of increasing size recover an increasing prefix of the most prevalent atoms in the training data. This "recovery principle" yields three testable predictions: many features in small SAEs are shared by all larger SAEs, SAEs trained on different data share features prevalent in both, and sufficiently large SAEs recover both parent and child features. In contrast to conventional wisdom that SAE features are unstable and "split" as size increases, we find that these predictions hold on SAEs of sizes ranging from 512 to 131,072 trained on two large embedding models. From a theoretical perspective, our results suggest the promise of a scientific theory of representations based on atomic features. Practically, our results suggest the promise of scaling SAEs.

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2610.05794 [cs.AI]

(or arXiv:2610.05794v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.05794 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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