75 citations · 326 across the 20 of their papers we have counts for
6 papers · 1 filter
Learning to Navigate Wikipedia by Taking Random Walks
Manzil Zaheer, Kenneth Marino, Will Grathwohl +7
A fundamental ability of an intelligent web-based agent is seeking out and acquiring new information. Internet search engines reliably find the correct vicinity but the top results…
Transformers generalize differently from information stored in context vs in weights
Stephanie C. Y. Chan, Ishita Dasgupta, Junkyung Kim +3
Transformer models can use two fundamentally different kinds of information: information stored in weights during training, and information provided ``in-context'' at inference tim…
Language models show human-like content effects on reasoning tasks
Ishita Dasgupta, Andrew K. Lampinen, Stephanie C. Y. Chan +5
Reasoning is a key ability for an intelligent system. Large language models (LMs) achieve above-chance performance on abstract reasoning tasks, but exhibit many imperfections. Howe…
Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines
Sreejan Kumar, Carlos G. Correa, Ishita Dasgupta +7
Strong inductive biases give humans the ability to quickly learn to perform a variety of tasks. Although meta-learning is a method to endow neural networks with useful inductive bi…
Can language models learn from explanations in context?
Andrew K. Lampinen, Ishita Dasgupta, Stephanie C. Y. Chan +6
Language Models (LMs) can perform new tasks by adapting to a few in-context examples. For humans, explanations that connect examples to task principles can improve learning. We the…
Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning
Sreejan Kumar, Ishita Dasgupta, Nathaniel D. Daw +2
The ability to acquire abstract knowledge is a hallmark of human intelligence and is believed by many to be one of the core differences between humans and neural network models. Ag…