1 citations · 1 across the 8 of their papers we have counts for
10 papers
Children, but not language models, show accelerating returns in word learning
Michael C. Frank
Children learn hundreds of words over the first years of their lives, in a process that begins slowly but quickly picks up speed. Prior models describe vocabulary growth as evidenc…
LEVANTE-bench: Multi-Scale Comparison of VLMs to Children Using Cognitive Tasks (or, "Is Your VLM Smarter Than a 5th Grader?")
Alvin Wei Ming Tan, David Cardinal, Tania Lorido-Botran +3
Given the inherently multimodal nature of human experience, vision-language models (VLMs) hold substantial promise for modeling human cognition as it grows and develops with experi…
EgoBabyVLM: Benchmarking Cross-Modal Learning from Naturalistic Egocentric Video Data
Dongyan Lin, Phillip Rust, Angel Villar Corrales +19
Children acquire language grounding with remarkable robustness from limited visuo-linguistic input in ways that surpass today's best large multimodal models. Recent research sugges…
Characterizing the visual representation of objects from the child's view
Jane Yang, Tarun Sepuri, Alvin Wei Ming Tan +3
Children acquire object category representations from their everyday experiences in the first few years of life. What do the inputs to this learning process look like? We analyzed…
Zero-shot World Models Are Developmentally Efficient Learners
Khai Loong Aw, Klemen Kotar, Wanhee Lee +6
Young children demonstrate early abilities to understand their physical world, estimating depth, motion, object coherence, interactions, and many other aspects of physical scene un…
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…