Divergent Creativity in Humans and Large Language Models

Creativity is a multifaceted construct at the crossroads of individual expression, problem solving, and innovation. Human creativity is pivotal in shaping cultures and has undergone continuous transformation across historical epochs. Our understanding of this ability is now influencing the landscape of artificial intelligence and cognitive systems (1-5). In the past few years, the advent of sophisticated Large Language Models (LLMs) has spurred considerable interest in evaluating their capabilities and apparent human-like traits (6), particularly in terms of their impacts on human creative processes (7, 8). However, the so-called creative abilities of modern LLMs have yet to be systematically evaluated and compared to humans on benchmarking tasks that are suitable for both. Although the ability to generate novel and aesthetically pleasing artifacts has long been considered a uniquely human attribute, this view has been challenged by the recent advances in generative AI. This technological progress has ignited discussions surrounding the creative capabilities of machines (9-12), ushering in the emerging field of computational creativity--a multidisciplinary domain that explores the potential of artificial systems to exhibit creativity in a manner analogous to human cognition. The release of GPT-4 was marked with an exceptional gain in performance across various standardized benchmarks (13). Demonstrating its versatility in language-and vision-based tasks, GPT-4 has successfully passed a uniform bar examination, the SAT, and multiple AP exams, transcending the boundaries of traditional AI capabilities.