Human versus Machine Creativity: A Large-Scale Comparison of The Divergent Thinking Ability in Humans and Large Language Models

主题及内容简介：
Title:
Human versus Machine Creativity: A Large-Scale Comparison of The Divergent Thinking Ability in Humans and Large Language Models

Abstract:
Artificial intelligence (AI) research traditionally focuses on knowledge-based problem-solving. Yet, as Einstein observed, “Imagination is more important than knowledge. Knowledge is limited. Imagination encircles the world.” As human-machine partnerships play an increasing role in addressing grand societal challenges, a deeper understanding of the comparative strengths of human and machine creativity is needed. This research examines human and large language model (LLM) creativity using the divergent association task, which assesses the ability to generate novel ideas. We compared the creativity test results of approximately 10,000 human participants with over 200,000 observations from various LLMs. Our findings reveal three key results. First, the average creativity level of humans is slightly higher than that of LLMs. Second, increasing the "creativity" parameter of LLMs enhanced performance up to a threshold, beyond which outputs became nonsensical. Third, attempts to improve LLM performance through prompt engineering yielded mixed to negative results. We discuss the implications of these fundamental differences in human and LLM creativity for human-machine collaboration.
主讲人简介：
黄棣芳，中国科学院数学与系统科学研究院助理研究员。他的主要研究领域涵盖金融科技和人工智能，研究成果发表在自然科学和金融管理领域期刊，如PNAS、PNAS Nexus、Nature Human Behaviour、Management Science、Journal of Accounting Research、Journal of Financial and Quantitative Analysis、Econometric Reviews等