14 citations · 18 across the 3 of their papers we have counts for
5 papers · 1 filter
Collateral facilitation in humans and language models
James A. Michaelov, Benjamin K. Bergen
Are the predictions of humans and language models affected by similar things? Research suggests that while comprehending language, humans make predictions about upcoming words, wit…
Contextualized Sensorimotor Norms: multi-dimensional measures of sensorimotor strength for ambiguous English words, in context
Sean Trott, Benjamin Bergen
Most large language models are trained on linguistic input alone, yet humans appear to ground their understanding of words in sensorimotor experience. A natural solution is to augm…
Word Acquisition in Neural Language Models
Tyler A. Chang, Benjamin K. Bergen
We investigate how neural language models acquire individual words during training, extracting learning curves and ages of acquisition for over 600 words on the MacArthur-Bates Com…
Different kinds of cognitive plausibility: why are transformers better than RNNs at predicting N400 amplitude?
James A. Michaelov, Megan D. Bardolph, Seana Coulson +1
Despite being designed for performance rather than cognitive plausibility, transformer language models have been found to be better at predicting metrics used to assess human langu…
RAW-C: Relatedness of Ambiguous Words--in Context (A New Lexical Resource for English)
Sean Trott, Benjamin Bergen
Most words are ambiguous--i.e., they convey distinct meanings in different contexts--and even the meanings of unambiguous words are context-dependent. Both phenomena present a chal…