用理论指导大模型生成跨领域科研创意，效果优于传统方法
LLMs can Realize Combinatorial Creativity: Generating Creative Ideas via LLMs for Scientific Research
- 基于组合创意理论构建框架，实现跨领域知识连接
- 在OAG-Bench上生成创意相似度提升7%-10%
- 适合需要突破性思路的科研人员和AI辅助创新研究
科学创意生成在创造力理论与计算创造力研究中已有深入探讨，为理解与实现创造性过程提供了重要框架。然而，近期利用大语言模型（LLMs）进行研究创意生成的工作常忽视这些理论基础。本文提出一个显式实现组合创意理论的框架，包含通用抽象层级的知识检索系统与结构化组合流程。检索系统通过映射不同抽象层次的概念，实现跨领域知识的有意义关联；组合流程则系统分析并重组组件，生成新颖解决方案。在OAG-Bench数据集上的实验表明，该框架在生成与真实研究发展一致的创意方面表现优异，多个指标下相似度得分提升7%–10%。结果有力证明，在适当理论指导下，大模型可有效实现组合创意，推动AI辅助科研实践，并深化对机器创造力的理解。
原文摘要 · Abstract (English)
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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