Large language models (LLMs) have recently soared in popularity due to their ease of access and the unprecedented intelligence exhibited on diverse applications. However, LLMs like ChatGPT present significant limitations in supporting complex information tasks due to the insufficient affordances of the text-based medium and linear conversational structure. Through a formative study with ten participants, we found that LLM interfaces often present long-winded responses, making it difficult for people to quickly comprehend and interact flexibly with various pieces of information, particularly during more complex tasks. We present Graphologue, an interactive system that converts text-based responses from LLMs into graphical diagrams to facilitate information-seeking and question-answering tasks. Graphologue employs novel prompting strategies and interface designs to extract entities and relationships from LLM responses and constructs node-link diagrams in real-time. Further, users can interact with the diagrams to flexibly adjust the graphical presentation and to submit context-specific prompts to obtain more information. Utilizing diagrams, Graphologue enables graphical, non-linear dialogues between humans and LLMs, facilitating information exploration, organization, and comprehension.

翻译：摘要：大型语言模型（LLMs）因易于访问及其在多种应用中展现出的前所未有的智能性，近期迅速普及。然而，诸如ChatGPT等LLMs在支持复杂信息任务方面存在显著局限，这源于基于文本的媒介与线性对话结构所提供的交互功能不足。通过一项涉及十名参与者的形成性研究，我们发现LLMs界面常呈现冗长的回答，导致人们难以快速理解并灵活交互各类信息，尤其在处理更复杂任务时。我们提出Graphologue，一个交互式系统，该系统能将LLMs生成的基于文本的回答转化为图形化图表，以促进信息检索与问答任务。Graphologue采用新颖的提示策略与界面设计，从LLM回答中提取实体与关系，并实时构建节点-链接图。此外，用户可与图表交互以灵活调整图形化呈现方式，并提交上下文相关的提示以获取更多信息。通过利用图表，Graphologue实现了人类与LLMs之间的图形化、非线性对话，从而促进信息探索、整理与理解。