110 citations · 242 across the 15 of their papers we have counts for
18 papers
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…
On the Effect of Anticipation on Reading Times
Tiago Pimentel, Clara Meister, Ethan G. Wilcox +2
Over the past two decades, numerous studies have demonstrated how less predictable (i.e., higher surprisal) words take more time to read. In general, these studies have implicitly…
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…
A Targeted Assessment of Incremental Processing in Neural LanguageModels and Humans
Ethan Gotlieb Wilcox, Pranali Vani, Roger P. Levy
We present a targeted, scaled-up comparison of incremental processing in humans and neural language models by collecting by-word reaction time data for sixteen different syntactic…
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…
Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models
Ethan Wilcox, Peng Qian, Richard Futrell +3
Humans can learn structural properties about a word from minimal experience, and deploy their learned syntactic representations uniformly in different grammatical contexts. We asse…
On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior
Ethan Gotlieb Wilcox, Jon Gauthier, Jennifer Hu +2
Human reading behavior is tuned to the statistics of natural language: the time it takes human subjects to read a word can be predicted from estimates of the word's probability in…
A Systematic Assessment of Syntactic Generalization in Neural Language Models
Jennifer Hu, Jon Gauthier, Peng Qian +2
While state-of-the-art neural network models continue to achieve lower perplexity scores on language modeling benchmarks, it remains unknown whether optimizing for broad-coverage p…
Representation of Constituents in Neural Language Models: Coordination Phrase as a Case Study
Aixiu An, Peng Qian, Ethan Wilcox +1
Neural language models have achieved state-of-the-art performances on many NLP tasks, and recently have been shown to learn a number of hierarchically-sensitive syntactic dependenc…
Hierarchical Representation in Neural Language Models: Suppression and Recovery of Expectations
Ethan Wilcox, Roger Levy, Richard Futrell
Deep learning sequence models have led to a marked increase in performance for a range of Natural Language Processing tasks, but it remains an open question whether they are able t…
What Syntactic Structures block Dependencies in RNN Language Models?
Ethan Wilcox, Roger Levy, Richard Futrell
Recurrent Neural Networks (RNNs) trained on a language modeling task have been shown to acquire a number of non-local grammatical dependencies with some success. Here, we provide n…
Structural Supervision Improves Learning of Non-Local Grammatical Dependencies
Ethan Wilcox, Peng Qian, Richard Futrell +2
State-of-the-art LSTM language models trained on large corpora learn sequential contingencies in impressive detail and have been shown to acquire a number of non-local grammatical…
Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic State
Richard Futrell, Ethan Wilcox, Takashi Morita +3
We deploy the methods of controlled psycholinguistic experimentation to shed light on the extent to which the behavior of neural network language models reflects incremental repres…
RNNs as psycholinguistic subjects: Syntactic state and grammatical dependency
Richard Futrell, Ethan Wilcox, Takashi Morita +1
Recurrent neural networks (RNNs) are the state of the art in sequence modeling for natural language. However, it remains poorly understood what grammatical characteristics of natur…
What do RNN Language Models Learn about Filler-Gap Dependencies?
Ethan Wilcox, Roger Levy, Takashi Morita +1
RNN language models have achieved state-of-the-art perplexity results and have proven useful in a suite of NLP tasks, but it is as yet unclear what syntactic generalizations they l…