Supporting Sensemaking of Large Language Model Outputs at Scale
Katy Ilonka Gero Chelse Swoopes Ziwei Gu Jonathan K. Kummerfeld Elena L. Glassman
Large language models (LLMs) are capable of generating multiple responses to
a single prompt, yet little effort has been expended to help end-users or
system designers make use of this capability. In this paper, we explore how to
present many LLM responses at once. We design five features, which include both
pre-existing and novel methods for computing similarities and differences
across textual documents, as well as how to render their outputs. We report on
a controlled user study (n=24) and eight case studies evaluating these features
and how they support users in different tasks. We find that the features
support a wide variety of sensemaking tasks and even make tasks previously
considered to be too difficult by our participants now tractable. Finally, we
present design guidelines to inform future explorations of new LLM interfaces.