  Language models (LMs) show promise as tools for communicating science to the
general public by simplifying and summarizing complex language. Because models
can be prompted to generate text for a specific audience (e.g.,
college-educated adults), LMs might be used to create multiple versions of
plain language summaries for people with different familiarities of scientific
topics. However, it is not clear what the benefits and pitfalls of adaptive
plain language are. When is simplifying necessary, what are the costs in doing
so, and do these costs differ for readers with different background knowledge?
Through three within-subjects studies in which we surface summaries for
different envisioned audiences to participants of different backgrounds, we
found that while simpler text led to the best reading experience for readers
with little to no familiarity in a topic, high familiarity readers tended to
ignore certain details in overly plain summaries (e.g., study limitations). Our
work provides methods and guidance on ways of adapting plain language summaries
beyond the single "general" audience.
