27 citations · 66 across the 10 of their papers we have counts for
11 papers · 1 filter
On the robustness of modeling grounded word learning through a child's egocentric input
Wai Keen Vong, Brenden M. Lake
What insights can machine learning bring to understanding human language acquisition? Large language and multimodal models have achieved remarkable capabilities, but their reliance…
Do different prompting methods yield a common task representation in language models?
Guy Davidson, Todd M. Gureckis, Brenden M. Lake +1
Demonstrations and instructions are two primary approaches for prompting language models to perform in-context learning (ICL) tasks. Do identical tasks elicited in different ways r…
Word meaning in minds and machines
Brenden M. Lake, Gregory L. Murphy
Machines have achieved a broad and growing set of linguistic competencies, thanks to recent progress in Natural Language Processing (NLP). Psychologists have shown increasing inter…
Learning word-referent mappings and concepts from raw inputs
Wai Keen Vong, Brenden M. Lake
How do children learn correspondences between the language and the world from noisy, ambiguous, naturalistic input? One hypothesis is via cross-situational learning: tracking words…
A Benchmark for Systematic Generalization in Grounded Language Understanding
Laura Ruis, Jacob Andreas, Marco Baroni +2
Humans easily interpret expressions that describe unfamiliar situations composed from familiar parts ("greet the pink brontosaurus by the ferris wheel"). Modern neural networks, by…
Mutual exclusivity as a challenge for deep neural networks
Kanishk Gandhi, Brenden M. Lake
Strong inductive biases allow children to learn in fast and adaptable ways. Children use the mutual exclusivity (ME) bias to help disambiguate how words map to referents, assuming…