大语言模型可实现组合创造性：通过大语言模型为科学研究生成创意
LLMs can Realize Combinatorial Creativity: Generating Creative Ideas via LLMs for Scientific Research
AI总结：
针对LLMs生成科研创意忽视理论基础的问题，本研究提出基于组合创造性理论的框架，通过泛化级检索系统和结构化组合过程生成创意，在OAG-Bench数据集上较基线相似性得分提升7%-10%，推动AI辅助研究并深化机器创造性理论理解。
摘要：
科学创意生成在创造力理论和计算创造力研究中已被广泛研究，为理解和实现创造性过程提供了有价值的框架。然而，近期使用大语言模型（LLMs）进行研究创意生成的工作往往忽视了这些理论基础。我们提出一个明确使用LLMs实现组合创造性理论的框架，其特点是包含用于跨域知识发现的泛化级检索系统和用于创意生成的结构化组合过程。检索系统在不同抽象层级映射概念，以实现不同领域间的有意义关联；组合过程则系统分析并重组组件以生成新颖解决方案。在OAG-Bench数据集上的实验证明了我们框架的有效性，在生成与真实研究进展一致的创意方面持续优于基线方法（在多个指标上相似性得分提升7%-10%）。我们的结果为LLMs在适当理论框架指导下可有效实现组合创造性提供了有力证据，既推动了AI辅助研究的实际进展，也促进了对机器创造性的理论理解。
英文摘要：
Scientific idea generation has been extensively studied in creativity theory and computational creativity research, providing valuable frameworks for understanding and implementing creative processes. However, recent work using Large Language Models (LLMs) for research idea generation often overlooks these theoretical foundations. We present a framework that explicitly implements combinatorial creativity theory using LLMs, featuring a generalization-level retrieval system for cross-domain knowledge discovery and a structured combinatorial process for idea generation. The retrieval system maps concepts across different abstraction levels to enable meaningful connections between disparate domains, while the combinatorial process systematically analyzes and recombines components to generate novel solutions. Experiments on the OAG-Bench dataset demonstrate our framework's effectiveness, consistently outperforming baseline approaches in generating ideas that align with real research developments (improving similarity scores by 7\%-10\% across multiple metrics). Our results provide strong evidence that LLMs can effectively realize combinatorial creativity when guided by appropriate theoretical frameworks, contributing both to practical advancement of AI-assisted research and theoretical understanding of machine creativity.
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