3 citations · 5 across the 9 of their papers we have counts for
6 papers · 1 filter
Scaling cross-situational word learning on children's naturalistic experiences
Michael C. Frank, Alvin W. M. Tan, Khai Loong Aw +9
How do children learn the meanings of the simple, concrete words that form a majority of their early vocabulary? Statistical information about many word meanings can be recovered f…
Which one is banana man? Evaluating vision-language models in multi-turn pragmatic interpretation
Alvin Wei Ming Tan, Ben Prystawski, Veronica Boyce
Flexible adaptation to context and shared pragmatic intuitions contribute to smooth human conversation. Iterated reference games---in which players repeatedly pick out novel refere…
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…
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…