Abstract
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.- Anthology ID:
- 2024.findings-emnlp.700
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2024
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 11978–11994
- Language:
- URL:
- https://aclanthology.org/2024.findings-emnlp.700
- DOI:
- 10.18653/v1/2024.findings-emnlp.700
- Cite (ACL):
- Lakshmi Nair, Evana Gizzi, and Jivko Sinapov. 2024. Creative Problem Solving in Large Language and Vision Models - What Would it Take?. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 11978–11994, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Creative Problem Solving in Large Language and Vision Models - What Would it Take? (Nair et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.findings-emnlp.700.pdf