Détail du document
Identifiant
oai:arXiv.org:2411.03575
Sujet
Computer Science - Human-Computer ...Auteur
Sandholm, Thomas Dong, Sarah Mukherjee, Sayandev Feland, John Huberman, Bernardo A.Catégorie
Computer Science
Année
2024
Date de référencement
13/11/2024
Mots clés
generations explorationRésumé
We present a novel AI-based ideation assistant and evaluate it in a user study with a group of innovators.
The key contribution of our work is twofold: we propose a method of idea exploration in a constrained domain by means of LLM-supported semantic navigation of problem and solution spaces, and employ novel automated data input filtering to improve generations.
We found that semantic exploration is preferred to the traditional prompt-output interactions, measured both in explicit survey rankings, and in terms of innovation assistant engagement, where 2.1x more generations were performed using semantic exploration.
We also show that filtering input data with metrics such as relevancy, coherence and human alignment leads to improved generations in the same metrics as well as enhanced quality of experience among innovators.
;Comment: arXiv admin note: text overlap with arXiv:2402.06053
Sandholm, Thomas,Dong, Sarah,Mukherjee, Sayandev,Feland, John,Huberman, Bernardo A., 2024, Semantic Navigation for AI-assisted Ideation
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