Language Models and Babies as Language Learners - Understanding Human and Machine Learning

Explore a fascinating lecture that delves into the significance of "baby" language models (BabyLMs) in advancing academic research across machine learning, linguistics, and cognitive science. Learn how these smaller, more manageable models serve as valuable tools for studying fundamental questions about language acquisition in both humans and machines. Discover insights from the BabyLM Challenge and recent research investigating critical periods of second language learning, inductive bias in human language patterns, and word learning trajectories. Understand the unique advantages of BabyLMs over larger language models, including their cost-effectiveness, training efficiency, and closer simulation of human learning processes. Examine how studying these non-human language learners provides valuable perspectives on human language acquisition and helps define humanity's position within the broader spectrum of possible learning systems.