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.

研究了如何同时呈现多个大型语言模型的响应，设计了包括计算文本文档之间相似性和差异性的预存和新方法以及如何渲染它们的输出的五个特征，通过对用户研究和八个案例研究的评估发现这些特征支持多样的意义理解任务，并使之前被认为过于困难的任务现在变得可行，最后提出了设计指南以指导未来对新型大型语言模型界面的探索。