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
- 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)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2023.conll-babylm.1.pdf