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One of the exciting capabilities of recent language models for dialog istheir ability to independently search for relevant information to ground agiven dialog response. However, obtaining training data to teach models how toissue search queries is time and resource consuming. In this work, we proposeq2d: an automatic data generation pipeline that generates information-seekingdialogs from questions. We prompt a large language model (PaLM) to createconversational versions of question answering datasets, and use it to improvequery generation models that communicate with external search APIs to grounddialog responses. Unlike previous approaches which relied on human writtendialogs with search queries, our method allows to automatically generatequery-based grounded dialogs with better control and scale. Our experimentsdemonstrate that: (1) For query generation on the QReCC dataset, models trainedon our synthetically-generated data achieve 90%--97% of the performance ofmodels trained on the human-generated data; (2) We can successfully generatedata for training dialog models in new domains without any existing dialog dataas demonstrated on the multi-hop MuSiQue and Bamboogle QA datasets. (3) Weperform a thorough analysis of the generated dialogs showing that humans findthem of high quality and struggle to distinguish them from human-writtendialogs.

q2d: Turning Questions into Dialogs to Teach Models How to Search