大语言模型展现出与人类相当的个体与集体创造力
Large Language Models show both individual and collective creativity comparable to humans
AI总结：
本研究通过13项跨领域创造性任务测试发现，Claude、GPT-4等顶尖大语言模型个体创造力达人类第52百分位，多次提问的集体创造力可比肩8-10人，未来最优应用下或匹敌人类小群体。
摘要：
迄今为止，人工智能主要实现了常规任务的自动化，但若大语言模型（Large Language Models，LLMs）展现出与人类相当的创造力，这对工作的未来意味着什么？为全面衡量LLMs的创造力，本研究采用了横跨三个领域的13项创造性任务。我们将LLMs与人类个体进行基准测试，还采用了一种新颖的方法，将其与人类群体的集体创造力进行对比。研究发现，表现最优的LLMs（Claude和GPT-4）在与人类对比时排名处于第52百分位；总体而言，LLMs在发散思维和问题解决方面表现出色，但在创意写作方面存在差距。当对一个LLM提问10次时，其集体创造力相当于8到10个人类的水平；当要求生成更多回复时，LLMs每多生成2个额外回复，就相当于多1个人类的创造力贡献。最终，若得到最优应用，LLMs未来可能在工作场景中与一小群人类相匹敌。
英文摘要：
Artificial intelligence has, so far, largely automated routine tasks, but what does it mean for the future of work if Large Language Models (LLMs) show creativity comparable to humans? To measure the creativity of LLMs holistically, the current study uses 13 creative tasks spanning three domains. We benchmark the LLMs against individual humans, and also take a novel approach by comparing them to the collective creativity of groups of humans. We find that the best LLMs (Claude and GPT-4) rank in the 52nd percentile against humans, and overall LLMs excel in divergent thinking and problem solving but lag in creative writing. When questioned 10 times, an LLM's collective creativity is equivalent to 8-10 humans. When more responses are requested, two additional responses of LLMs equal one extra human. Ultimately, LLMs, when optimally applied, may compete with a small group of humans in the future of work.
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。
