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        <title>Humans and transformer LMs: Abstraction drives language learning</title>
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    <abstract>Categorization is a core component of human linguistic competence. We investigate how a transformer-based language model (LM) learns linguistic categories by comparing its behaviour over the course of training to behaviours which characterize abstract feature–based and concrete exemplar–based accounts of human language acquisition. We investigate how lexical semantic and syntactic categories emerge using novel divergence-based metrics that track learning trajectories using next-token distributions. In experiments with GPT-2 small, we find that (i) when a construction is learned, abstract class-level behaviour is evident at earlier steps than lexical item–specific behaviour, and (ii) that different linguistic behaviours emerge abruptly in sequence at different points in training, revealing that abstraction plays a key role in how LMs learn. This result informs the models of human language acquisition that LMs may serve as an existence proof for.</abstract>
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%0 Conference Proceedings
%T Humans and transformer LMs: Abstraction drives language learning
%A Jian, Jasper
%A Manning, Christopher D.
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F jian-manning-2026-humans
%X Categorization is a core component of human linguistic competence. We investigate how a transformer-based language model (LM) learns linguistic categories by comparing its behaviour over the course of training to behaviours which characterize abstract feature–based and concrete exemplar–based accounts of human language acquisition. We investigate how lexical semantic and syntactic categories emerge using novel divergence-based metrics that track learning trajectories using next-token distributions. In experiments with GPT-2 small, we find that (i) when a construction is learned, abstract class-level behaviour is evident at earlier steps than lexical item–specific behaviour, and (ii) that different linguistic behaviours emerge abruptly in sequence at different points in training, revealing that abstraction plays a key role in how LMs learn. This result informs the models of human language acquisition that LMs may serve as an existence proof for.
%U https://aclanthology.org/2026.eacl-long.32/
%P 752-765

Markdown (Informal)

[Humans and transformer LMs: Abstraction drives language learning](https://aclanthology.org/2026.eacl-long.32/) (Jian & Manning, EACL 2026)

• Humans and transformer LMs: Abstraction drives language learning (Jian & Manning, EACL 2026)

ACL

• Jasper Jian and Christopher D Manning. 2026. Humans and transformer LMs: Abstraction drives language learning. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 752–765, Rabat, Morocco. Association for Computational Linguistics.