Modeling child language acquisition: Evaluating the impact of Curriculum Learning on the grammatical generalization of Italian BabyLMs
Year: 2026
Type: article
Abstract: BabyLMs are neural models which aim to simulate child language acquisition by training on developmentally plausible corpora. In this framework, several strategies have been ex- plored to bridge the gap between machine and human language learning, most notably Curriculum Learning - a training approach that presents examples to a neural network in order of increasing linguistic complexity. While numerous ordering criteria have been pro- posed, this study leverages four distinct metrics - two based on lexical diversity and two on syntactic complexity. We investigate whether exposing models to language in a manner similar to human development yields improvements in the generalization of linguistic abil- ities.
Author: Maccione, Davide
Language: English
Subfield: Developmental and Educational Psychology
Field: Psychology
Domain: Social Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W7217645116