On the Interpretability of Whisper Encodings Using Sparse Autoencoders

Abstract:While deep transformer-based models have advanced rapidly, their internal mechanisms remain largely a mystery. Recent work has prioritized understanding text-based transformer models, leaving ASR systems largely unexplored. In order to address this gap, we examine the internal representations of Whisper's encoder using a sparse autoencoder. We find diverse monosemantic features across linguistic and non-linguistic boundaries, spanning a hierarchy from phonetic to semantic representations, and conduct a causal feature-steering campaign across this hierarchy, including cross-lingual steering. We further find that steering is more reliable for higher-level features than lower-level ones, an asymmetry that may reflect redundant encoding of lower-level information. Altogether, this work demonstrates that Whisper's encoder represents a surprisingly rich hierarchy of linguistic information that extends well beyond what is strictly necessary for transcription.

Computation and Language (cs.CL)

arXiv:2605.12225 [cs.CL]

(or arXiv:2605.12225v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.12225 Focus to learn more arXiv-issued DOI via DataCite

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