Why it Matters That Babies and Language Models are the Only Known Language Learners

Small language models trained on limited data can provide economical, controlled simulations for studying human language acquisition. Alex Warstadt discusses the BabyLM Challenge and its goals of improving data efficiency, cognitive plausibility and accessibility. Studies from his UC San Diego lab train models from scratch under controlled conditions to investigate critical periods in second-language learning, inductive biases behind cross-linguistic patterns, and distributional signatures of word-learning trajectories. Comparing cases where these models resemble human learners with cases where they diverge helps locate human language acquisition within a broader space of possible learning systems. The approach supports fundamental work across machine learning, linguistics and cognitive science.