  Scientific jargon can impede researchers when they read materials from other
domains. Current methods of jargon identification mainly use corpus-level
familiarity indicators (e.g., Simple Wikipedia represents plain language).
However, researchers' familiarity of a term can vary greatly based on their own
background. We collect a dataset of over 10K term familiarity annotations from
11 computer science researchers for terms drawn from 100 paper abstracts.
Analysis of this data reveals that jargon familiarity and information needs
vary widely across annotators, even within the same sub-domain (e.g., NLP). We
investigate features representing individual, sub-domain, and domain knowledge
to predict individual jargon familiarity. We compare supervised and
prompt-based approaches, finding that prompt-based methods including personal
publications yields the highest accuracy, though zero-shot prompting provides a
strong baseline. This research offers insight into features and methods to
integrate personal data into scientific jargon identification.
