3 citations · 6 across the 10 of their papers we have counts for
11 papers
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…
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…
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, including whether multilingual acquisition leads to delays in…
Assessing the alignment between infants' visual and linguistic experience using multimodal language models
Alvin Wei Ming Tan, Jane Yang, Tarun Sepuri +6
Figuring out which objects or concepts words refer to is a central language learning challenge for young children. Most models of this process posit that children learn early objec…
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…
DevBench: A multimodal developmental benchmark for language learning
Alvin Wei Ming Tan, Sunny Yu, Bria Long +5
How (dis)similar are the learning trajectories of vision-language models and children? Recent modeling work has attempted to understand the gap between models' and humans' data eff…