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Beyond Top Words: MonoTM for Topic Modeling with Interpretable Monosemantic Features

This story is from 2026-09-10. It is preserved in the archive; the latest stories are on the live feed.

arXiv:2609.09575v1 Announce Type: new Abstract: Topic models summarize large text corpora, but top-ranked words often provide only a limited representation of topic semantics. Sparse autoencoders (SAEs) offer a way to move beyond word-level descriptors by extracting interpretable features from dens…

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  1. 2026-09-10 04:00 · arXiv cs.CL
    Beyond Top Words: MonoTM for Topic Modeling with Interpretable Monosemantic Features

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