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In this work, we advocate planning as a useful intermediate representation for rendering conditional generation less opaque and more grounded. We propose a new conceptualization of text plans as a sequence of question-answer (QA) pairs and enhance existing datasets (e.g., for summarization) with a QA blueprint operating as a proxy for content selection (i.e., what to say) and planning (i.e., in what order). We obtain blueprints automatically by exploiting state-of-the-art question generation technology and convert input-output pairs into input-blueprint-output tuples. We develop Transformer-based models, each varying in how they incorporate the blueprint in the generated output (e.g., as a global plan or iteratively). Evaluation across metrics and datasets demonstrates that blueprint models are more factual than alternatives which do not resort to planning and allow tighter control of the generation output.<\/jats:p>","DOI":"10.1162\/tacl_a_00583","type":"journal-article","created":{"date-parts":[[2023,8,16]],"date-time":"2023-08-16T19:47:13Z","timestamp":1692215233000},"page":"974-996","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":10,"title":["Conditional Generation with a Question-Answering Blueprint"],"prefix":"10.1162","volume":"11","author":[{"given":"Shashi","family":"Narayan","sequence":"first","affiliation":[{"name":"Google DeepMind, UK. shashinarayan@google.com"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joshua","family":"Maynez","sequence":"additional","affiliation":[{"name":"Google DeepMind, UK. joshuahm@google.com"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reinald 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