  Research-paper blog posts help scientists disseminate their work to a larger
audience, but translating papers into this format requires substantial
additional effort. Blog post creation is not simply transforming a long-form
article into a short output, as studied in most prior work on human-AI
summarization. In contrast, blog posts are typically full-length articles that
require a combination of strategic planning grounded in the source document,
well-organized drafting, and thoughtful revisions. Can tools powered by large
language models (LLMs) assist scientists in writing research-paper blog posts?
To investigate this question, we conducted a formative study (N=6) to
understand the main challenges of writing such blog posts with an LLM: high
interaction costs for 1) reviewing and utilizing the paper content and 2)
recurrent sub-tasks of generating and modifying the long-form output. To
address these challenges, we developed Papers-to-Posts, an LLM-powered tool
that implements a new Plan-Draft-Revise workflow, which 1) leverages an LLM to
generate bullet points from the full paper to help users find and select
content to include (Plan) and 2) provides default yet customizable LLM
instructions for generating and modifying text (Draft, Revise). Through a
within-subjects lab study (N=20) and between-subjects deployment study (N=37
blog posts, 26 participants) in which participants wrote blog posts about their
papers, we compared Papers-to-Posts to a strong baseline tool that provides an
LLM-generated draft and access to free-form LLM prompting. Results show that
Papers-to-Posts helped researchers to 1) write significantly more satisfying
blog posts and make significantly more changes to their blog posts in a fixed
amount of time without a significant change in cognitive load (lab) and 2) make
more changes to their blog posts for a fixed number of writing actions
(deployment).
