4 papers
Baby Scale: Investigating Models Trained on Individual Children's Language Input
Steven Y. Feng, Alvin W. M. Tan, Michael C. Frank
Modern language models (LMs) must be trained on many orders of magnitude more words of training data than human children receive before they begin to produce useful behavior. Asses…
Bringing Up a Bilingual BabyLM: Investigating Multilingual Language Acquisition Using Small-Scale Models
Linda Zeng, Steven Y. Feng, Michael C. Frank
Multilingualism is incredibly common around the world, leading to many important theoretical and practical questions about how children learn multiple languages at once. For exampl…
Is Child-Directed Speech Effective Training Data for Language Models?
Steven Y. Feng, Noah D. Goodman, Michael C. Frank
While high-performing language models are typically trained on hundreds of billions of words, human children become fluent language users with a much smaller amount of data. What a…
The BabyView dataset: High-resolution egocentric videos of infants' and young children's everyday experiences
Bria Long, Robert Z. Sparks, Violet Xiang +9
Human children far exceed modern machine learning algorithms in their sample efficiency, achieving high performance in key domains with much less data than current models. This ''d…