Directed Diversity: Leveraging Language Embedding Distances for Collective Creativity in Crowd Ideation

要旨

Crowdsourcing can collect many diverse ideas by prompting ideators individually, but this can generate redundant ideas. Prior methods reduce redundancy by presenting peers’ ideas or peer-proposed prompts, but these require much human coordination. We introduce Directed Diversity, an automatic prompt selection approach that leverages language model embedding distances to maximize diversity. Ideators can be directed towards diverse prompts and away from prior ideas, thus improving their collective creativity. Since there are diverse metrics of diversity, we present a Diversity Prompting Evaluation Framework consolidating metrics from several research disciplines to analyze along the ideation chain — prompt selection, prompt creativity, prompt-ideation mediation, and ideation creativity. Using this framework, we evaluated Directed Diversity in a series of a simulation study and four user studies for the use case of crowdsourcing motivational messages to encourage physical activity. We show that automated diverse prompting can variously improve collective creativity across many nuanced metrics of diversity.

著者
Samuel Rhys. Cox
National University of Singapore, Singapore, Singapore, Singapore
Yunlong Wang
National University of Singapore, Singapore, Singapore
Ashraf Abdul
National University of Singapore, Singapore, --- Select One ---, Singapore
Christian von der Weth
National University of Singapore, Singapore, Singapore
Brian Y. Lim
National University of Singapore, Singapore, Singapore
DOI

10.1145/3411764.3445782

論文URL

https://doi.org/10.1145/3411764.3445782

動画

会議: CHI 2021

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2021.acm.org/)

セッション: Computational Human-AI Conversation

[A] Paper Room 02, 2021-05-11 17:00:00~2021-05-11 19:00:00 / [B] Paper Room 02, 2021-05-12 01:00:00~2021-05-12 03:00:00 / [C] Paper Room 02, 2021-05-12 09:00:00~2021-05-12 11:00:00
Paper Room 02
14 件の発表
2021-05-11 17:00:00
2021-05-11 19:00:00
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