Séminaire ayant lieu dans le cadre du Doctorat en informatique cognitive, en collaboration avec le centre de recherche CRIA             

TITRE : Data efficiency in children and language models

Michel. C. FRANK

Jeudi le 1er octobre 2026 à 10h30

Local PK-5115 (Il est possible d'y assister en virtuel en vous inscrivant ici)               

RÉSUMÉ

Large language models show intriguing emergent behaviors, yet they receive at least three to four — and sometimes as much as six — orders of magnitude more language data than human children. What accounts for this vast difference in sample efficiency? I will describe steps towards a paradigm in which we can address this question. In particular, I'll discuss the use of egocentric video ("baby headcam") data for model training, and the use of developmental data for model evaluation. This paradigm provides a model-based framework for exploring the nature of children's early development.

BIOGRAPHIE

Michael C. FRANK is Benjamin Scott Crocker Professor of Human Biology in the Department of Psychology at Stanford University and Director of the Symbolic Systems Program. He received his PhD from MIT in Brain and Cognitive Sciences in 2010. He studies children's language learning and development, with a focus on the use of large-scale datasets to understand the variability and consistency of learning across cultures. He is a founder of the ManyBabies Consortium, and has led open-data projects including Wordbank and the ongoing LEVANTE project. He has received awards including the Troland Award from the National Academy of Sciences and the FABBS Early Career Impact award. He served as President of the Cognitive Science Society, has edited for journals including Cognition and Child Development, and is current co-Editor in Chief of the Open Encyclopedia of Cognitive Science.

RÉFÉRENCES 

Frank, M. C. (2026). Children, but not language models, show accelerating returns in word learning. https://arxiv.org/abs/2608.17120

Frank, M. C., & Goodman, N. D. (2025). Cognitive modeling using artificial intelligence. Annual Review of Psychology. doi:10.1146/annurev-psych-030625-040748.

Frank, M. C. (2023). Bridging the data gap between children and large language models. Trends in Cognitive Sciences. https://psyarxiv.com/qzbgx/