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Large Language Models as Innovators: A Framework to Leverage Latent Space Exploration for Novelty Discovery

Created by
  • Haebom

Author

Mateusz Bystro nski, Miko{\l}aj Ho{\l}ysz, Grzegorz Piotrowski, Nitesh V. Chawla, Tomasz Kajdanowicz

Outline

In this paper, we propose a model-agnostic latent space idea generation framework to overcome the limitations of large-scale language models (LLMs) in their creative idea generation capabilities. Unlike existing domain-specific heuristics or structured prompt pipeline approaches, our approach enables controllable and scalable creativity through continuous embedding space exploration that is easily applicable to a variety of domains, input formats, and creative tasks without manual rules. In this paper, we introduce an early prototype of our method and present the conceptual framework and preliminary results demonstrating its potential as a general-purpose joint idea generator for human-AI collaboration.

Takeaways, Limitations

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Takeaways:
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Presenting a creative idea generation framework applicable to various LLMs and domains through a model-agnostic approach.
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We present the possibility of achieving controllable and scalable creativity through continuous embedding space exploration without manual rules.
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Confirming its potential as a universal joint idea generator for human-AI collaboration.
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Limitations:
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As this is an early stage prototype, further research is needed for practical application and performance verification.
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Further research is needed on the efficiency and optimization of latent space exploration.
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Further experiments and analyses are needed to evaluate the generalization performance of the proposed framework and its applicability to various creative tasks.
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