Humans and transformer LMs: Abstraction drives language learning
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 ...
| Authors | Jasper Jian, Christopher D. Manning |
|---|---|
| Primary category | cs.CL |
| Published | 18 March 2026 |
| Last updated at source | 18 March 2026 |
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