@inproceedings{bondielli-etal-2026-parts,
title = "Parts-of-Speech as Emergent Categories in {SAE} Latent Space",
author = "Bondielli, Alessandro and
Passaro, Lucia and
Auriemma, Serena and
Lenci, Alessandro",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2656/",
pages = "60465--60481",
ISBN = "979-8-89176-459-0",
abstract = "Sparse AutoEncoders (SAEs) offer a promising way to inspect language model representations, but it is still unclear what kind of linguistic structure their latents expose. We use part-of-speech (PoS) categories as a controlled test case to study whether morpho-syntactic information is encoded by individual latents or by structured groups of features. We find that PoS distinctions are highly recoverable from SAE activations, but do not align with one-to-one latent / category mappings. This recoverability is not reducible to lexical memorisation, and Open and Closed PoS classes differ substantially. Categories are supported by compact groups of sparse latents, with substantial variation across tags. These groups remain stable on held-out data, while also showing overlap between related categories. Our results show that SAEs localise morpho-syntactic information in a distributed and category-dependent form rather than through atomic grammatical features."
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%0 Conference Proceedings
%T Parts-of-Speech as Emergent Categories in SAE Latent Space
%A Bondielli, Alessandro
%A Passaro, Lucia
%A Auriemma, Serena
%A Lenci, Alessandro
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F bondielli-etal-2026-parts
%X Sparse AutoEncoders (SAEs) offer a promising way to inspect language model representations, but it is still unclear what kind of linguistic structure their latents expose. We use part-of-speech (PoS) categories as a controlled test case to study whether morpho-syntactic information is encoded by individual latents or by structured groups of features. We find that PoS distinctions are highly recoverable from SAE activations, but do not align with one-to-one latent / category mappings. This recoverability is not reducible to lexical memorisation, and Open and Closed PoS classes differ substantially. Categories are supported by compact groups of sparse latents, with substantial variation across tags. These groups remain stable on held-out data, while also showing overlap between related categories. Our results show that SAEs localise morpho-syntactic information in a distributed and category-dependent form rather than through atomic grammatical features.
%U https://aclanthology.org/2026.emnlp-main.2656/
%P 60465-60481
Markdown (Informal)
[Parts-of-Speech as Emergent Categories in SAE Latent Space](https://aclanthology.org/2026.emnlp-main.2656/) (Bondielli et al., EMNLP 2026)
ACL
- Alessandro Bondielli, Lucia Passaro, Serena Auriemma, and Alessandro Lenci. 2026. Parts-of-Speech as Emergent Categories in SAE Latent Space. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 60465–60481, Budapest, Hungary. Association for Computational Linguistics.