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Rapid AIdeation: Generating Ideas With the Self and in Collaboration With Large Language Models

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arxiv 2403.12928 v1 pith:3Z7IZHJQ submitted 2024-03-19 cs.HC

classification cs.HC
keywords ideaslargeparticipantsqualitygenailanguageobservedrapid
verification ladder T0 review T1 audit T2 compute T3 formal
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Generative artificial intelligence (GenAI) can rapidly produce large and diverse volumes of content. This lends to it a quality of creativity which can be empowering in the early stages of design. In seeking to understand how creative ways to address practical issues can be conceived between humans and GenAI, we conducted a rapid ideation workshop with 21 participants where they used a large language model (LLM) to brainstorm potential solutions and evaluate them. We found that the LLM produced a greater variety of ideas that were of high quality, though not necessarily of higher quality than human-generated ideas. Participants typically prompted in a straightforward manner with concise instructions. We also observed two collaborative dynamics with the LLM fulfilling a consulting role or an assisting role depending on the goals of the users. Notably, we observed an atypical anti-collaboration dynamic where participants used an antagonistic approach to prompt the LLM.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 4 citations worldwide. Full citation record

  1. Creativity in LLM-based Multi-Agent Systems: A Survey

    cs.HC 2025-05 conditional novelty 6.0 of 10

    A taxonomy-driven survey organizes the emerging field of creativity in LLM-based multi-agent systems across workflows, techniques, personas, datasets, and evaluation metrics.

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