The recent surge in the capabilities of Large Language Models (LLMs) has led
to claims that they are approaching a level of creativity akin to human
capabilities. This idea has sparked a blend of excitement and apprehension.
However, a critical piece that has been missing in this discourse is a
systematic evaluation of LLM creativity, particularly in comparison to human
divergent thinking. To bridge this gap, we leverage recent advances in
creativity science to build a framework for in-depth analysis of divergent
creativity in both state-of-the-art LLMs and a substantial dataset of 100,000
humans. We found evidence suggesting that LLMs can indeed surpass human
capabilities in specific creative tasks such as divergent association and
creative writing. Our quantitative benchmarking framework opens up new paths
for the development of more creative LLMs, but it also encourages more granular
inquiries into the distinctive elements that constitute human inventive thought
processes, compared to those that can be artificially generated.

最新研究表明，大型语言模型（LLMs）在创造性方面可以超越人类的能力，特别是在发散性思维和创造性写作等特定创造性任务方面，这项研究使用创造力科学的最新进展构建了一个评估 LLMs 创造力的深入分析框架与一个包含 10 万人的实验数据集进行对比，既为更具创造力的 LLMs 的发展开辟了新的路径，也促进了对由人类独特的创新思维过程构成的与那些可以人工生成的元素的更细致的研究。