  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.
