1 citations · 2 across the 4 of their papers we have counts for
5 papers
Nothing from Something: Can a Language Model Discover 0?
Phoebe Zeng, Thomas L. Griffiths, Brenden M. Lake
AI systems based on artificial neural networks are being developed with aspirations of pushing the boundary of human mathematical knowledge. A key question for these systems is how…
Whither symbols in the era of advanced neural networks?
Thomas L. Griffiths, Brenden M. Lake, R. Thomas McCoy +2
Some of the strongest evidence that human minds should be thought about in terms of symbolic systems has been the way they combine ideas, produce novelty, and learn quickly. We arg…
Rapid Word Learning Through Meta In-Context Learning
Wentao Wang, Guangyuan Jiang, Tal Linzen +1
Humans can quickly learn a new word from a few illustrative examples, and then systematically and flexibly use it in novel contexts. Yet the abilities of current language models fo…
CoLLEGe: Concept Embedding Generation for Large Language Models
Ryan Teehan, Brenden Lake, Mengye Ren
Current language models are unable to quickly learn new concepts on the fly, often requiring a more involved finetuning process to learn robustly. Prompting in-context is not robus…
Comparing Abstraction in Humans and Large Language Models Using Multimodal Serial Reproduction
Sreejan Kumar, Raja Marjieh, Byron Zhang +5
Humans extract useful abstractions of the world from noisy sensory data. Serial reproduction allows us to study how people construe the world through a paradigm similar to the game…