  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 implies that additional scaling
could further expand the range of capabilities of language models.
