机遇由设计：评估跨领域映射对人类和LLM创造力的影响
Assessing the Effect of Cross-Domain Mapping on Creativity in Humans and Large Language Models
AI总结：
研究通过对比人类与LLM在跨领域映射和用户需求两种提示下的创造力表现，发现人类从随机远程领域获得灵感能提升创造力，而LLM生成原创想法更多且跨领域映射影响随灵感源与目标的语义距离增加而增强。
摘要：
大语言模型（LLM）是否以与人类相同的方式展现创造力，以及相同干预措施能否同时提升人类和LLM的创造力？本文评估了一种有潜力但尚未广泛测试的创造力干预措施：强制创作者从随机的远程领域源领域中引申类比（'跨领域映射'）。人类参与者和LLM在两种提示下生成日常产品的创新特性：（i）跨领域映射，要求将属性从随机分配的源领域（如章鱼、仙人掌、GPS）转换；（ii）用户需求，要求提出针对未满足用户需求的创新。结果显示，人类在随机分配的跨领域映射下表现可靠，而LLM平均生成更多原创想法，且不显示显著的跨领域映射效应。然而，在两种系统中，当灵感源与目标的语义距离增加时，跨领域映射的影响也随之增强。本研究突显了远程关联在创造性构思中的作用以及人类和LLM对相同创造力干预措施的系统性差异。
英文摘要：
Creative ideas often arise by associating remote concepts. Can random associations reliably increase originality, and do they help humans and large language models (LLMs) in the same way? We asked human participants and seven LLMs to design products by drawing inspiration from a random source or addressing an unmet user need. Humans reliably benefited from cross-domain mappings, while LLMs generated more original ideas than humans but showed no overall benefit from the intervention, though this changed with semantic distance. More distant source-target pairings produced more original ideas in both humans and LLMs. Humans benefited at nearly any distance, while only the highest-rated LLMs benefited when the source was sufficiently remote. Humans and LLMs also used sources differently: humans tended to transfer surface features, while LLMs transferred structural and functional properties. These findings reveal the generative role of remote associations and systematic differences in how humans and AI respond to the same creativity intervention.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
