  Re-finding information is an essential activity, however, it can be difficult
when people struggle to express what they are looking for. Through a
need-finding survey, we first seek opportunities for improving re-finding
experiences, and explore one of these opportunities by implementing the
FoundWright system. The system leverages recent advances in language
transformer models to expand people's ability to express what they are looking
for, through the interactive creation and manipulation of concepts contained
within documents. We use FoundWright as a design probe to understand (1) how
people create and use concepts, (2) how this expanded ability helps re-finding,
and (3) how people engage and collaborate with FoundWright's machine learning
support. Our probe reveals that this expanded way of expressing re-finding
goals helps people with the task, by complementing traditional searching and
browsing. Finally, we present insights and recommendations for future work
aiming at developing systems to support re-finding.
