Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora

Alex Warstadt, Aaron Mueller, Leshem Choshen, Ethan Wilcox, Chengxu Zhuang, Juan Ciro, Rafael Mosquera, Bhargavi Paranjabe, Adina Williams, Tal Linzen, Ryan Cotterell


Anthology ID:
2023.conll-babylm.1
Volume:
Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning
Month:
December
Year:
2023
Address:
Singapore
Editors:
Alex Warstadt, Aaron Mueller, Leshem Choshen, Ethan Wilcox, Chengxu Zhuang, Juan Ciro, Rafael Mosquera, Bhargavi Paranjabe, Adina Williams, Tal Linzen, Ryan Cotterell
Venue:
CoNLL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–34
Language:
URL:
https://aclanthology.org/2023.conll-babylm.1
DOI:
10.18653/v1/2023.conll-babylm.1
Bibkey:
Cite (ACL):
Alex Warstadt, Aaron Mueller, Leshem Choshen, Ethan Wilcox, Chengxu Zhuang, Juan Ciro, Rafael Mosquera, Bhargavi Paranjabe, Adina Williams, Tal Linzen, and Ryan Cotterell. 2023. Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora. In Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning, pages 1–34, Singapore. Association for Computational Linguistics.
Cite (Informal):
Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora (Warstadt et al., CoNLL 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.conll-babylm.1.pdf