  We study how to apply large language models to write grounded and organized
long-form articles from scratch, with comparable breadth and depth to Wikipedia
pages. This underexplored problem poses new challenges at the pre-writing
stage, including how to research the topic and prepare an outline prior to
writing. We propose STORM, a writing system for the Synthesis of Topic Outlines
through Retrieval and Multi-perspective Question Asking. STORM models the
pre-writing stage by (1) discovering diverse perspectives in researching the
given topic, (2) simulating conversations where writers carrying different
perspectives pose questions to a topic expert grounded on trusted Internet
sources, (3) curating the collected information to create an outline.
  For evaluation, we curate FreshWiki, a dataset of recent high-quality
Wikipedia articles, and formulate outline assessments to evaluate the
pre-writing stage. We further gather feedback from experienced Wikipedia
editors. Compared to articles generated by an outline-driven
retrieval-augmented baseline, more of STORM's articles are deemed to be
organized (by a 25% absolute increase) and broad in coverage (by 10%). The
expert feedback also helps identify new challenges for generating grounded long
articles, such as source bias transfer and over-association of unrelated facts.
