  One of the exciting capabilities of recent language models for dialog is
their ability to independently search for relevant information to ground a
given dialog response. However, obtaining training data to teach models how to
issue search queries is time and resource consuming. In this work, we propose
q2d: an automatic data generation pipeline that generates information-seeking
dialogs from questions. We prompt a large language model (PaLM) to create
conversational versions of question answering datasets, and use it to improve
query generation models that communicate with external search APIs to ground
dialog responses. Unlike previous approaches which relied on human written
dialogs with search queries, our method allows to automatically generate
query-based grounded dialogs with better control and scale. Our experiments
demonstrate that: (1) For query generation on the QReCC dataset, models trained
on our synthetically-generated data achieve 90%--97% of the performance of
models trained on the human-generated data; (2) We can successfully generate
data for training dialog models in new domains without any existing dialog data
as demonstrated on the multi-hop MuSiQue and Bamboogle QA datasets. (3) We
perform a thorough analysis of the generated dialogs showing that humans find
them of high quality and struggle to distinguish them from human-written
dialogs.
