A Preliminary Look at Generative AI for the Creation of Abstract Verbal-to-visual Analogies

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dc.contributor.authorCombs, Kara
dc.contributor.authorBihl, Trevor
dc.date.accessioned2023-12-26T18:36:48Z
dc.date.available2023-12-26T18:36:48Z
dc.date.issued2024-01-03
dc.identifier.doihttps://doi.org/10.24251/HICSS.2024.143
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.other290b3aee-ea14-420c-8532-3b6209eea5da
dc.identifier.urihttps://hdl.handle.net/10125/106520
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDecision Making with Sustainable, Fair and Trustworthy AI
dc.subjectanalogy
dc.subjectartificial intelligence
dc.subjectdecisions
dc.subjectdeep learning
dc.subjectgenerative ai
dc.titleA Preliminary Look at Generative AI for the Creation of Abstract Verbal-to-visual Analogies
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractGenerative artificial intelligence (GAI) appears useful in the creation of new data, which assists in the expansion of small, limited datasets in fields such as analogical reasoning (AR). This multidisciplinary study expands the number of AR visual datasets within the field of visual question answering. We introduce the first visual analogy dataset that includes abstract concepts by leveraging three text-to-image GAI generators, Text2Img, Craiyon, and Midjourney, to produce images for antonym and synonym analogies. Our visual dataset achieves up to 70% accuracy and performs better 84.6% of the time compared to the same evaluation on only textual information. Interestingly, results also imply that paid GAI services produce higher accuracy. This work shows the potential for GAI to aid in the development of abstract visual analogy datasets, which allows for a better understanding and incorporation of AR into cognitive-inspired AI models capable of analogy-based information fusion.
dcterms.extent10 pages
prism.startingpage1180

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Decision Making with Sustainable, Fair and Trustworthy AI