76 citations · 120 across the 14 of their papers we have counts for
6 papers · 1 filter
Rarely a problem? Language models exhibit inverse scaling in their predictions following few-type quantifiers
James A. Michaelov, Benjamin K. Bergen
How well do language models deal with quantification? In this study, we focus on 'few'-type quantifiers, as in 'few children like toys', which might pose a particular challenge for…
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…
Do Large Language Models know what humans know?
Sean Trott, Cameron Jones, Tyler Chang +2
Humans can attribute beliefs to others. However, it is unknown to what extent this ability results from an innate biological endowment or from experience accrued through child deve…
Do language models make human-like predictions about the coreferents of Italian anaphoric zero pronouns?
James A. Michaelov, Benjamin K. Bergen
Some languages allow arguments to be omitted in certain contexts. Yet human language comprehenders reliably infer the intended referents of these zero pronouns, in part because the…
The Geometry of Multilingual Language Model Representations
Tyler A. Chang, Zhuowen Tu, Benjamin K. Bergen
We assess how multilingual language models maintain a shared multilingual representation space while still encoding language-sensitive information in each language. Using XLM-R as…
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…