“Larger Language Models Do In-Context Learning Differently”, 2023-03-07 ():
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 (eg. ‘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.
For further details, refer to the primary sources available on OpenAI’s repository and Google’s publication.