This paper discusses approaches for integrating Computational Creativity (CC) with research in large language and vision models (LLVMs) to address the key limitation of these models in creative problem solving.
The authors first provide an overview of how LLVMs are typically used in task planning, highlighting the potential entry points for introducing creative problem solving capabilities. They then discuss how principles from CC literature, specifically Boden's three forms of creativity (exploratory, combinational, and transformational), can be extended to augment the embedding spaces of LLVMs for enabling creative problem solving.
The authors present preliminary experiments demonstrating the application of transformational creativity, where they show that providing information about object affordances in prompts can improve the ability of LLVMs to creatively replace missing objects. However, they note that the full integration of all three forms of creativity is likely necessary for effective creative problem solving in LLVMs.
The paper emphasizes the need for a deeper integration of CC and ML research, as creative problem solving is not only a key limitation of current LLVMs but also potentially linked to the broader goal of Artificial General Intelligence (AGI). The authors hope this work will encourage discussions on creative problem solving and CC within the ML community.
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by Lakshmi Nair... a las arxiv.org 05-03-2024
https://arxiv.org/pdf/2405.01453.pdfConsultas más profundas