16 citations · 30 across the 7 of their papers we have counts for
6 papers · 1 filter
Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora
Alex Warstadt, Aaron Mueller, Leshem Choshen +8
Children can acquire language from less than 100 million words of input. Large language models are far less data-efficient: they typically require 3 or 4 orders of magnitude more d…
Incremental Sentence Processing Mechanisms in Autoregressive Transformer Language Models
Michael Hanna, Aaron Mueller
Autoregressive transformer language models (LMs) possess strong syntactic abilities, often successfully handling phenomena from agreement to NPI licensing. However, the features th…
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…
How to Plant Trees in Language Models: Data and Architectural Effects on the Emergence of Syntactic Inductive Biases
Aaron Mueller, Tal Linzen
Accurate syntactic representations are essential for robust generalization in natural language. Recent work has found that pre-training can teach language models to rely on hierarc…
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…
What Do NLP Researchers Believe? Results of the NLP Community Metasurvey
Julian Michael, Ari Holtzman, Alicia Parrish +8
We present the results of the NLP Community Metasurvey. Run from May to June 2022, the survey elicited opinions on controversial issues, including industry influence in the field,…