72 citations · 243 across the 30 of their papers we have counts for
9 papers · 1 filter
Findings of the Second BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora
Michael Y. Hu, Aaron Mueller, Candace Ross +7
The BabyLM Challenge is a community effort to close the data-efficiency gap between human and computational language learners. Participants compete to optimize language model train…
Surprise! Uniform Information Density Isn't the Whole Story: Predicting Surprisal Contours in Long-form Discourse
Eleftheria Tsipidi, Franz Nowak, Ryan Cotterell +3
The Uniform Information Density (UID) hypothesis posits that speakers tend to distribute information evenly across linguistic units to achieve efficient communication. Of course, i…
Reverse-Engineering the Reader
Samuel Kiegeland, Ethan Gotlieb Wilcox, Afra Amini +2
Numerous previous studies have sought to determine to what extent language models, pretrained on natural language text, can serve as useful models of human cognition. In this paper…
Generalized Measures of Anticipation and Responsivity in Online Language Processing
Mario Giulianelli, Andreas Opedal, Ryan Cotterell
We introduce a generalization of classic information-theoretic measures of predictive uncertainty in online language processing, based on the simulation of expected continuations o…
Thesis proposal: Are We Losing Textual Diversity to Natural Language Processing?
Josef Jon
This thesis argues that the currently widely used Natural Language Processing algorithms possibly have various limitations related to the properties of the texts they handle and pr…
On the Role of Context in Reading Time Prediction
Andreas Opedal, Eleanor Chodroff, Ryan Cotterell +1
We present a new perspective on how readers integrate context during real-time language comprehension. Our proposals build on surprisal theory, which posits that the processing eff…
Investigating Critical Period Effects in Language Acquisition through Neural Language Models
Ionut Constantinescu, Tiago Pimentel, Ryan Cotterell +1
Humans appear to have a critical period (CP) for language acquisition: Second language (L2) acquisition becomes harder after early childhood, and ceasing exposure to a first langua…
The Role of -gram Smoothing in the Age of Neural Networks
Luca Malagutti, Andrius Buinovskij, Anej Svete +3
For nearly three decades, language models derived from the -gram assumption held the state of the art on the task. The key to their success lay in the application of various smo…
[Call for Papers] The 2nd BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus
Leshem Choshen, Ryan Cotterell, Michael Y. Hu +7
After last year's successful BabyLM Challenge, the competition will be hosted again in 2024/2025. The overarching goals of the challenge remain the same; however, some of the compe…