Creative Problem Solving in Large Language and Vision Models -- What Would it Take?
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916418498330624 |
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| author | Nair, Lakshmi Gizzi, Evana Sinapov, Jivko |
| author_facet | Nair, Lakshmi Gizzi, Evana Sinapov, Jivko |
| contents | We advocate for a strong integration of Computational Creativity (CC) with research in large language and vision models (LLVMs) to address a key limitation of these models, i.e., creative problem solving. We present preliminary experiments showing how CC principles can be applied to address this limitation. Our goal is to foster discussions on creative problem solving in LLVMs and CC at prestigious ML venues. Our code is available at: https://github.com/lnairGT/creative-problem-solving-LLMs |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_01453 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Creative Problem Solving in Large Language and Vision Models -- What Would it Take? Nair, Lakshmi Gizzi, Evana Sinapov, Jivko Artificial Intelligence Machine Learning We advocate for a strong integration of Computational Creativity (CC) with research in large language and vision models (LLVMs) to address a key limitation of these models, i.e., creative problem solving. We present preliminary experiments showing how CC principles can be applied to address this limitation. Our goal is to foster discussions on creative problem solving in LLVMs and CC at prestigious ML venues. Our code is available at: https://github.com/lnairGT/creative-problem-solving-LLMs |
| title | Creative Problem Solving in Large Language and Vision Models -- What Would it Take? |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2405.01453 |