GoKawiilCognitive scientists probe why children learn language with far less data than AI models
Researchers highlight a stark 'data efficiency gap': large language models require orders of magnitude more text than a human child hears to master language basics. Stanford's Michael Frank and Georgetown's Ethan Wilcox note that while frontier models train on trillions of tokens, children pick up language fundamentals from a fraction of that input within their first year or two of life. Scientists are studying how kids achieve this to both understand human cognition and potentially inform more efficient AI training methods.