Humans and transformer LMs: Abstraction drives language learning

• URL: http://arxiv.org/abs/2603.17475v1
• Date: Wed, 18 Mar 2026 08:30:20 GMT
• Title: Humans and transformer LMs: Abstraction drives language learning
• Abstract summary: We investigate how a transformer-based language model learns linguistic categories.<n>We find that when a construction is learned, abstract class-level behaviour is evident at earlier steps than lexical item-specific behaviour.
• Score: 32.87902816038439
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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