Large language models (LLMs) have recently soared in popularity due to their ease of access and the unprecedented ability to synthesize text responses to diverse user questions. 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：近期，大语言模型（LLMs）因易于获取以及能够综合生成针对用户多样化问题的文本回复这一前所未有的能力而广受欢迎。然而，诸如ChatGPT之类的大语言模型，由于基于文本的媒介和线性对话结构所提供的支持不足，在支撑复杂信息任务方面存在显著局限。通过一项针对十名参与者的形成性研究，我们发现大语言模型界面常呈现冗长的回复，这使得人们难以快速理解各类信息并与之灵活交互，尤其是在处理更复杂的任务时。我们提出了Graphologue，一个交互式系统，该系统将大语言模型基于文本的回复转换为图形化图表，以促进信息检索和问答任务。Graphologue采用新颖的提示策略和界面设计，从大语言模型回复中提取实体与关系，并实时构建节点-链接图。此外，用户可与图表进行交互，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。借助图表，Graphologue实现了人类与大语言模型之间图形化、非线性的对话，促进了信息的探索、组织与理解。