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Large language models (LLMs) have recently soared in popularity due to theirease of access and the unprecedented ability to synthesize text responses todiverse user questions. However, LLMs like ChatGPT present significantlimitations in supporting complex information tasks due to the insufficientaffordances of the text-based medium and linear conversational structure.Through a formative study with ten participants, we found that LLM interfacesoften present long-winded responses, making it difficult for people to quicklycomprehend and interact flexibly with various pieces of information,particularly during more complex tasks. We present Graphologue, an interactivesystem that converts text-based responses from LLMs into graphical diagrams tofacilitate information-seeking and question-answering tasks. Graphologueemploys novel prompting strategies and interface designs to extract entitiesand relationships from LLM responses and constructs node-link diagrams inreal-time. Further, users can interact with the diagrams to flexibly adjust thegraphical presentation and to submit context-specific prompts to obtain moreinformation. Utilizing diagrams, Graphologue enables graphical, non-lineardialogues between humans and LLMs, facilitating information exploration,organization, and comprehension.

Graphologue: Exploring Large Language Model Responses with Interactive Diagrams