  This paper surveys and organizes research works in a new paradigm in natural
language processing, which we dub "prompt-based learning". Unlike traditional
supervised learning, which trains a model to take in an input x and predict an
output y as P(y|x), prompt-based learning is based on language models that
model the probability of text directly. To use these models to perform
prediction tasks, the original input x is modified using a template into a
textual string prompt x' that has some unfilled slots, and then the language
model is used to probabilistically fill the unfilled information to obtain a
final string x, from which the final output y can be derived. This framework is
powerful and attractive for a number of reasons: it allows the language model
to be pre-trained on massive amounts of raw text, and by defining a new
prompting function the model is able to perform few-shot or even zero-shot
learning, adapting to new scenarios with few or no labeled data. In this paper
we introduce the basics of this promising paradigm, describe a unified set of
mathematical notations that can cover a wide variety of existing work, and
organize existing work along several dimensions, e.g.the choice of pre-trained
models, prompts, and tuning strategies. To make the field more accessible to
interested beginners, we not only make a systematic review of existing works
and a highly structured typology of prompt-based concepts, but also release
other resources, e.g., a website http://pretrain.nlpedia.ai/ including
constantly-updated survey, and paperlist.
