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
Export citation
@inproceedings{warstadt-etal-2023-findings,
title = "Findings of the {B}aby{LM} Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora",
author = "Warstadt, Alex and
Mueller, Aaron and
Choshen, Leshem and
Wilcox, Ethan and
Zhuang, Chengxu and
Ciro, Juan and
Mosquera, Rafael and
Paranjabe, Bhargavi and
Williams, Adina and
Linzen, Tal and
Cotterell, Ryan",
editor = "Warstadt, Alex and
Mueller, Aaron and
Choshen, Leshem and
Wilcox, Ethan and
Zhuang, Chengxu and
Ciro, Juan and
Mosquera, Rafael and
Paranjabe, Bhargavi and
Williams, Adina and
Linzen, Tal and
Cotterell, Ryan",
booktitle = "Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.conll-babylm.1",
doi = "10.18653/v1/2023.conll-babylm.1",
pages = "1--34",
}
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%0 Conference Proceedings %T Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora %A Warstadt, Alex %A Mueller, Aaron %A Choshen, Leshem %A Wilcox, Ethan %A Zhuang, Chengxu %A Ciro, Juan %A Mosquera, Rafael %A Paranjabe, Bhargavi %A Williams, Adina %A Linzen, Tal %A Cotterell, Ryan %Y Warstadt, Alex %Y Mueller, Aaron %Y Choshen, Leshem %Y Wilcox, Ethan %Y Zhuang, Chengxu %Y Ciro, Juan %Y Mosquera, Rafael %Y Paranjabe, Bhargavi %Y Williams, Adina %Y Linzen, Tal %Y Cotterell, Ryan %S Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning %D 2023 %8 December %I Association for Computational Linguistics %C Singapore %F warstadt-etal-2023-findings %R 10.18653/v1/2023.conll-babylm.1 %U https://aclanthology.org/2023.conll-babylm.1 %U https://doi.org/10.18653/v1/2023.conll-babylm.1 %P 1-34
Markdown (Informal)
[Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora](https://aclanthology.org/2023.conll-babylm.1) (Warstadt et al., CoNLL 2023)
- Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora (Warstadt et al., CoNLL 2023)
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.