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Large language models (LLMs) are capable of generating multiple responses toa single prompt, yet little effort has been expended to help end-users orsystem designers make use of this capability. In this paper, we explore how topresent many LLM responses at once. We design five features, which include bothpre-existing and novel methods for computing similarities and differencesacross textual documents, as well as how to render their outputs. We report ona controlled user study (n=24) and eight case studies evaluating these featuresand how they support users in different tasks. We find that the featuressupport a wide variety of sensemaking tasks and even make tasks previouslyconsidered to be too difficult by our participants now tractable. Finally, wepresent design guidelines to inform future explorations of new LLM interfaces.

Supporting Sensemaking of Large Language Model Outputs at Scale