  Many recent studies on large-scale language models have reported successful
in-context zero- and few-shot learning ability. However, the in-depth analysis
of when in-context learning occurs is still lacking. For example, it is unknown
how in-context learning performance changes as the training corpus varies.
Here, we investigate the effects of the source and size of the pretraining
corpus on in-context learning in HyperCLOVA, a Korean-centric GPT-3 model. From
our in-depth investigation, we introduce the following observations: (1)
in-context learning performance heavily depends on the corpus domain source,
and the size of the pretraining corpus does not necessarily determine the
emergence of in-context learning, (2) in-context learning ability can emerge
when a language model is trained on a combination of multiple corpora, even
when each corpus does not result in in-context learning on its own, (3)
pretraining with a corpus related to a downstream task does not always
guarantee the competitive in-context learning performance of the downstream
task, especially in the few-shot setting, and (4) the relationship between
language modeling (measured in perplexity) and in-context learning does not
always correlate: e.g., low perplexity does not always imply high in-context
few-shot learning performance.
