  There are a few prominent practices for conducting reviews of academic
literature, including searching for specific keywords on Google Scholar or
checking citations from some initial seed paper(s). These approaches serve a
critical purpose for academic literature reviews, yet there remain challenges
in identifying relevant literature when similar work may utilize different
terminology (e.g., mixed-initiative visual analytics papers may not use the
same terminology as papers on model-steering, yet the two topics are relevant
to one another). In this paper, we introduce a system, VitaLITy, intended to
complement existing practices. In particular, VitaLITy promotes serendipitous
discovery of relevant literature using transformer language models, allowing
users to find semantically similar papers in a word embedding space given (1) a
list of input paper(s) or (2) a working abstract. VitaLITy visualizes this
document-level embedding space in an interactive 2-D scatterplot using
dimension reduction. VitaLITy also summarizes meta information about the
document corpus or search query, including keywords and co-authors, and allows
users to save and export papers for use in a literature review. We present
qualitative findings from an evaluation of VitaLITy, suggesting it can be a
promising complementary technique for conducting academic literature reviews.
Furthermore, we contribute data from 38 popular data visualization publication
venues in VitaLITy, and we provide scrapers for the open-source community to
continue to grow the list of supported venues.
