Graphologue: Exploring Large Language Model Responses with Interactive Diagrams
Informations
- Type:
- misc
- Auteurs:
- Peiling Jiang and Jude Rayan and Steven P. Dow and Haijun Xia
- Pertinence:
-
Haute
- Référence:
- jiang2023graphologue
- Doi:
- Mots-clés:
- Url:
- https://arxiv.org/abs/2305.11473
- Date de publication:
- 05/2023
- Résumé:
- chat + output des réponses du llm sous forme de diagram + menu contextuels pour en savoir plus
- Abstract:
- 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.
- Pdf:
- Lien pdf
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Auteurs
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| Nom | Nombre d'articles | Actions |
|---|---|---|
| Peiling Jiang and Jude Rayan and Steven P. Dow and Haijun Xia | 1 |