Large Language Models (LLMs) excel at countless tasks, yet struggle with creativity. In this paper, we introduce a novel approach that couples LLMs with structured representations and cognitively inspired manipulations to generate more creative and diverse ideas. Our notion of creativity goes beyond superficial token-level variations; rather, we explicitly recombine structured representations of existing ideas, allowing our algorithm to effectively explore the more abstract landscape of ideas. We demonstrate our approach in the culinary domain with DishCOVER, a model that generates creative recipes. Experiments comparing our model's results to those of GPT-4o show greater diversity. Domain expert evaluations reveal that our outputs, which are mostly coherent and feasible culinary creations, significantly surpass GPT-4o in terms of novelty, thus outperforming it in creative generation. We hope our work inspires further research into structured creativity in AI.

本研究针对大型语言模型（LLMs）在创意生成方面的不足，提出了一种新的方法，将LLMs与结构化表示和认知启发的操作相结合，以生成更具创意和多样性的想法。通过在烹饪领域展示的DishCOVER模型，我们的实验结果表明，该方法在生成新颖且可行的食谱方面显著超过了GPT-4o，推动了人工智能领域对结构化创造力的进一步研究。

激发创造力：一种基于认知启发的方法，通过结构化表示增强大语言模型的创造力