  We study how in-context learning (ICL) in language models is affected by
semantic priors versus input-label mappings. We investigate two setups-ICL with
flipped labels and ICL with semantically-unrelated labels-across various model
families (GPT-3, InstructGPT, Codex, PaLM, and Flan-PaLM). First, experiments
on ICL with flipped labels show that overriding semantic priors is an emergent
ability of model scale. While small language models ignore flipped labels
presented in-context and thus rely primarily on semantic priors from
pretraining, large models can override semantic priors when presented with
in-context exemplars that contradict priors, despite the stronger semantic
priors that larger models may hold. We next study semantically-unrelated label
ICL (SUL-ICL), in which labels are semantically unrelated to their inputs
(e.g., foo/bar instead of negative/positive), thereby forcing language models
to learn the input-label mappings shown in in-context exemplars in order to
perform the task. The ability to do SUL-ICL also emerges primarily with scale,
and large-enough language models can even perform linear classification in a
SUL-ICL setting. Finally, we evaluate instruction-tuned models and find that
instruction tuning strengthens both the use of semantic priors and the capacity
to learn input-label mappings, but more of the former.
