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 asemergent abilitiesof 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.