  Large language models (LLMs) exploit in-context learning (ICL) to solve tasks
with only a few demonstrations, but its mechanisms are not yet well-understood.
Some works suggest that LLMs only recall already learned concepts from
pre-training, while others hint that ICL performs implicit learning over
demonstrations. We characterize two ways through which ICL leverages
demonstrations. Task recognition (TR) captures the extent to which LLMs can
recognize a task through demonstrations -- even without ground-truth labels --
and apply their pre-trained priors, whereas task learning (TL) is the ability
to capture new input-label mappings unseen in pre-training. Using a wide range
of classification datasets and three LLM families (GPT-3, LLaMA and OPT), we
design controlled experiments to disentangle the roles of TR and TL in ICL. We
show that (1) models can achieve non-trivial performance with only TR, and TR
does not further improve with larger models or more demonstrations; (2) LLMs
acquire TL as the model scales, and TL's performance consistently improves with
more demonstrations in context. Our findings unravel two different forces
behind ICL and we advocate for discriminating them in future ICL research due
to their distinct nature.
