- Anthology ID:
- 2023.conll-babylm.13
- 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:
- 158–170
- Language:
- URL:
- https://aclanthology.org/2023.conll-babylm.13
- DOI:
- 10.18653/v1/2023.conll-babylm.13
- Bibkey:
- Cite (ACL):
- Zheyu Zhang, Han Yang, Bolei Ma, David Rügamer, and Ercong Nie. 2023. Baby’s CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models. In Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning, pages 158–170, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Baby’s CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models (Zhang et al., CoNLL 2023)
- Copy Citation:
- PDF:
- https://aclanthology.org/2023.conll-babylm.13.pdf
Export citation
@inproceedings{zhang-etal-2023-babys,
title = "Baby{'}s {C}o{T}hought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models",
author = {Zhang, Zheyu and
Yang, Han and
Ma, Bolei and
R{\"u}gamer, David and
Nie, Ercong},
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.13",
doi = "10.18653/v1/2023.conll-babylm.13",
pages = "158--170",
}
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%0 Conference Proceedings %T Baby’s CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models %A Zhang, Zheyu %A Yang, Han %A Ma, Bolei %A Rügamer, David %A Nie, Ercong %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 zhang-etal-2023-babys %R 10.18653/v1/2023.conll-babylm.13 %U https://aclanthology.org/2023.conll-babylm.13 %U https://doi.org/10.18653/v1/2023.conll-babylm.13 %P 158-170
Markdown (Informal)
[Baby’s CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models](https://aclanthology.org/2023.conll-babylm.13) (Zhang et al., CoNLL 2023)
- Baby’s CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models (Zhang et al., CoNLL 2023)
ACL
- Zheyu Zhang, Han Yang, Bolei Ma, David Rügamer, and Ercong Nie. 2023. Baby’s CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models. In Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning, pages 158–170, Singapore. Association for Computational Linguistics.