 There have recently been tremendous advances in language models, partly because they can perform tasks with strong performance via in-context learning (ICL), a process whereby models are prompted with a few examples of input-label pairs before performing the task on an unseen evaluation example. In general, models’ success at in-context learning is enabled by:

    Their use of semantic prior knowledge from pre-training to predict labels while following the format of in-context examples (e.g., seeing examples of movie reviews with “positive sentiment” and “negative sentiment” as labels and performing sentiment analysis using prior knowledge).
    Learning the input-label mappings in context from the presented examples (e.g., finding a pattern that positive reviews should be mapped to one label, and negative reviews should be mapped to a different label).

In “Larger language models do in-context learning differently”, we aim to learn about how these two factors (semantic priors and input-label mappings) interact with each other in ICL settings, especially with respect to the scale of the language model that’s used. We investigate two settings to study these two factors — ICL with flipped labels (flipped-label ICL) and ICL with semantically-unrelated labels (SUL-ICL). In flipped-label ICL, labels of in-context examples are flipped so that semantic priors and input-label mappings disagree with each other. In SUL-ICL, labels of in-context examples are replaced with words that are semantically unrelated to the task presented in-context. We found that overriding prior knowledge is an emergent ability of model scale, as is the ability to learn in-context with semantically-unrelated labels. We also found that instruction tuning strengthens the use of prior knowledge more than it increases the capacity to learn input-label mappings. 
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