通过联想思维策略增强大语言模型的创造力
Enhancing Creativity in Large Language Models through Associative Thinking Strategies
AI总结：
本研究探索联想思维策略对大语言模型创造力的提升作用，针对vGPT-4在三个领域开展创意任务实验，证实该技术可显著提升其回复原创性，为LLMs创意能力增强提供新路径。
摘要：
本文探讨了通过联想思维提升vGPT-4等大语言模型（LLMs）创造力的方法，联想思维是一种通过关联看似无关的概念产生创意的认知过程。已有研究发现联想思维策略能有效帮助人类提升创造力，但同类策略能否助力LLMs提升创造力仍有待充分探索。本研究调查了通过提示词引导LLMs关联不同概念能否增强其创意输出。研究聚焦产品设计、故事创作和营销三个领域，设计了创意任务以评估vGPT-4生成原创且实用内容的能力。通过促使模型形成新颖关联，本研究评估了联想思维提升LLMs创意能力的潜力。研究结果表明，运用联想思维技术可显著提升vGPT-4回复的原创性。
英文摘要：
This paper explores the enhancement of creativity in Large Language Models (LLMs) like vGPT-4 through associative thinking, a cognitive process where creative ideas emerge from linking seemingly unrelated concepts. Associative thinking strategies have been found to effectively help humans boost creativity. However, whether the same strategies can help LLMs become more creative remains under-explored. In this work, we investigate whether prompting LLMs to connect disparate concepts can augment their creative outputs. Focusing on three domains -- Product Design, Storytelling, and Marketing -- we introduce creativity tasks designed to assess vGPT-4's ability to generate original and useful content. By challenging the models to form novel associations, we evaluate the potential of associative thinking to enhance the creative capabilities of LLMs. Our findings show that leveraging associative thinking techniques can significantly improve the originality of vGPT-4's responses.
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。
