110 citations · 179 across the 9 of their papers we have counts for
16 papers · 1 filter
On the Efficacy of Sampling Adapters
Clara Meister, Tiago Pimentel, Luca Malagutti +2
Sampling is a common strategy for generating text from probabilistic models, yet standard ancestral sampling often results in text that is incoherent or ungrammatical. To alleviate…
Testing the Predictions of Surprisal Theory in 11 Languages
Ethan Gotlieb Wilcox, Tiago Pimentel, Clara Meister +2
A fundamental result in psycholinguistics is that less predictable words take a longer time to process. One theoretical explanation for this finding is Surprisal Theory (Hale, 2001…
Controlled Text Generation with Natural Language Instructions
Wangchunshu Zhou, Yuchen Eleanor Jiang, Ethan Wilcox +2
Large language models generate fluent texts and can follow natural language instructions to solve a wide range of tasks without task-specific training. Nevertheless, it is notoriou…
Call for Papers -- The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus
Alex Warstadt, Leshem Choshen, Aaron Mueller +3
We present the call for papers for the BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus. This shared task is intended for participants with an i…
Exhaustivity and anti-exhaustivity in the RSA framework: Testing the effect of prior beliefs
Alexandre Cremers, Ethan G. Wilcox, Benjamin Spector
During communication, the interpretation of utterances is sensitive to a listener's probabilistic prior beliefs, something which is captured by one currently influential model of p…
Investigating Novel Verb Learning in BERT: Selectional Preference Classes and Alternation-Based Syntactic Generalization
Tristan Thrush, Ethan Wilcox, Roger Levy
Previous studies investigating the syntactic abilities of deep learning models have not targeted the relationship between the strength of the grammatical generalization and the amo…