Emergent Abilities of Large Language Models
Abstract
Scaling up language models has been shown to predictably improve performance and sample efficiency on a wide range of downstream tasks. This paper instead discusses an unpredictable phenomenon that we refer to as emergent abilities of large language models. We consider an ability to be emergent if it is not present in smaller models but is present in larger models. Thus, emergent abilities cannot be predicted simply by extrapolating the performance of smaller models. The existence of such emergence raises the question of whether additional scaling could potentially further expand the range of capabilities of language models.
Cite
Text
Wei et al. "Emergent Abilities of Large Language Models." Transactions on Machine Learning Research, 2022.Markdown
[Wei et al. "Emergent Abilities of Large Language Models." Transactions on Machine Learning Research, 2022.](https://mlanthology.org/tmlr/2022/wei2022tmlr-emergent/)BibTeX
@article{wei2022tmlr-emergent,
title = {{Emergent Abilities of Large Language Models}},
author = {Wei, Jason and Tay, Yi and Bommasani, Rishi and Raffel, Colin and Zoph, Barret and Borgeaud, Sebastian and Yogatama, Dani and Bosma, Maarten and Zhou, Denny and Metzler, Donald and Chi, Ed H. and Hashimoto, Tatsunori and Vinyals, Oriol and Liang, Percy and Dean, Jeff and Fedus, William},
journal = {Transactions on Machine Learning Research},
year = {2022},
url = {https://mlanthology.org/tmlr/2022/wei2022tmlr-emergent/}
}