2025/03/14 by Bastian Bunzeck, Bunzeck, Bastian, Daniel Durán +3 · 4 citations
Neuroscience · Psychology · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Language Development and Disorders #Neurobiology of Language and Bilingualism #Phonetics and Phonology Research
paper · pdf · doi:10.48550/arxiv.2503.11593
openalex publication_date 2025/03/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We analyze the influence of utterance-level construction distributions in German child-directed/child-available speech on the resulting word-level, syntactic and semantic competence (and their underlying learning trajectories) in small LMs, which we train on a novel collection of developmentally plausible language data for German. We find that trajectories are surprisingly robust for markedly different distributions of constructions in the training data, which have little effect on final accuracies and almost no effect on global learning trajectories. While syntax learning benefits from more complex utterances, word-level learning culminates in better scores with more fragmentary utterances. We argue that LMs trained on developmentally plausible data can contribute to debates on how conducive different kinds of linguistic stimuli are to language learning.