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Surv."],"published-print":{"date-parts":[[2023,9,30]]},"abstract":"<jats:p>\n            This article surveys and organizes research works in a new paradigm in natural language processing, which we dub \u201cprompt-based learning.\u201d Unlike traditional supervised learning, which trains a model to take in an input\n            <jats:bold>\n              <jats:italic>x<\/jats:italic>\n            <\/jats:bold>\n            and predict an output\n            <jats:bold>\n              <jats:italic>y<\/jats:italic>\n            <\/jats:bold>\n            as\n            <jats:italic>P<\/jats:italic>\n            (\n            <jats:bold>\n              <jats:italic>y|x<\/jats:italic>\n            <\/jats:bold>\n            ), 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\n            <jats:bold>\n              <jats:italic>x<\/jats:italic>\n            <\/jats:bold>\n            is modified using a\n            <jats:italic>template<\/jats:italic>\n            into a textual string\n            <jats:italic>prompt<\/jats:italic>\n            <jats:bold>\n              <jats:italic>x\u2032<\/jats:italic>\n            <\/jats:bold>\n            that has some unfilled slots, and then the language model is used to probabilistically fill the unfilled information to obtain a final string\n            <jats:bold>\n              <jats:italic>x\u0302<\/jats:italic>\n            <\/jats:bold>\n            , from which the final output\n            <jats:bold>\n              <jats:italic>y<\/jats:italic>\n            <\/jats:bold>\n            can be derived. This framework is powerful and attractive for a number of reasons: It allows the language model to be\n            <jats:italic>pre-trained<\/jats:italic>\n            on massive amounts of raw text, and by defining a new prompting function the model is able to perform\n            <jats:italic>few-shot<\/jats:italic>\n            or even\n            <jats:italic>zero-shot<\/jats:italic>\n            learning, adapting to new scenarios with few or no labeled data. In this article, 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.,\u00a0the choice of pre-trained language 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\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"url\" xlink:href=\"http:\/\/pretrain.nlpedia.ai\/\">NLPedia\u2013Pretrain<\/jats:ext-link>\n            including constantly updated survey and paperlist.\n          <\/jats:p>","DOI":"10.1145\/3560815","type":"journal-article","created":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T13:22:13Z","timestamp":1663161733000},"page":"1-35","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2656,"title":["Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing"],"prefix":"10.1145","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9030-1875","authenticated-orcid":false,"given":"Pengfei","family":"Liu","sequence":"first","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, Pennsylvania, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6117-9417","authenticated-orcid":false,"given":"Weizhe","family":"Yuan","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, Pennsylvania, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0370-1238","authenticated-orcid":false,"given":"Jinlan","family":"Fu","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0315-6727","authenticated-orcid":false,"given":"Zhengbao","family":"Jiang","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, Pennsylvania, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6875-7443","authenticated-orcid":false,"given":"Hiroaki","family":"Hayashi","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, Pennsylvania, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2072-3789","authenticated-orcid":false,"given":"Graham","family":"Neubig","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, Pennsylvania, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,1,16]]},"reference":[{"key":"e_1_3_3_2_2","article-title":"HTLM: Hyper-text pre-training and prompting of language models","author":"Aghajanyan Armen","year":"2021","unstructured":"Armen Aghajanyan, Dmytro Okhonko, Mike Lewis, Mandar Joshi, Hu Xu, Gargi Ghosh, and Luke Zettlemoyer. 2021. 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