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.

翻译：大规模语言模型（LLMs）能够针对单一提示生成多种响应，但鲜有研究致力于帮助终端用户或系统设计者利用这一能力。本文探索如何同时呈现大量LLM响应。我们设计了五种特征，既包含计算文本文档间异同点的既有方法和创新方法，也涵盖如何呈现其输出结果。我们通过一项受控用户研究（n=24）和八项案例研究来评估这些特征及其对用户完成不同任务的辅助效果。研究发现，这些特征能够支持多种理解性任务，甚至使参与者此前认为过于困难的任务变得可行。最后，我们提出设计准则，为未来新型LLM界面的探索提供参考。