是什么塑造了机器的创造性思维？全面基准测试基础模型的创造力
What Shapes a Creative Machine Mind? Comprehensively Benchmarking Creativity in Foundation Models
AI总结：
针对现有基础模型创造力评估框架零散、缺乏理论依据的问题，提出基于社会科学U-O-S标准、区分聚合与发散创造力的C^2-Eval基准，实验分析了主流模型的创造能力权衡，为创意AI研究提供有效评估工具。
摘要：
基础模型（FMs）的飞速发展使其能力远超传统任务范畴。创造力长期以来被视为人类智能的标志与创新驱动力，在生成式基础模型时代，它正日益被公认为机器智能的关键维度，是对传统准确率衡量标准的补充。然而，现有的创造力评估框架仍较为零散，依赖的特设指标并未以成熟理论为坚实基础。为填补这一空白，我们提出C^2-Eval——一个用于统一评估基础模型创造力的整体性基准。C^2-Eval区分了两种互补的创造力形式：聚合式创造力（任务存在受限解，如代码生成）和发散式创造力（任务为开放式，如故事创作）。它采用源自社会科学理论的细粒度标准，围绕有用性（Usefulness）、原创性（Originality）和惊喜性（Surprise，简称U-O-S）评估这两个维度。通过对主流专有模型和开源模型开展大量实验，我们分析了它们在创造能力上的权衡关系。研究结果揭示了当前基础模型在实现机器创造性思维方面的优势与挑战，表明C^2-Eval是研究创意人工智能演进格局的有效工具。
英文摘要：
The meteoric rise of foundation models (FMs) has expanded their capabilities far beyond conventional tasks. Creativity, long regarded as a hallmark of human intelligence and a driver of innovation, is now increasingly recognized as a critical dimension of machine intelligence in the era of generative FMs, complementing traditional measures of accuracy. However, existing evaluation frameworks for creativity remain fragmented, relying on ad hoc metrics not firmly grounded in established theories. To address this gap, we introduce C^2-Eval, a holistic benchmark for unified assessment of creativity in FMs. C^2-Eval distinguishes between two complementary forms of creativity: convergent creativity, where tasks admit constrained solutions (e.g., code generation), and divergent creativity, where tasks are open-ended (e.g., storytelling). It evaluates both dimensions using fine-grained criteria derived from social-science theory, focusing on Usefulness, Originality, and Surprise (U-O-S). Through extensive experiments on leading proprietary and open-source models, we analyze trade-offs in their creative capabilities. Our results highlight both the strengths and challenges of current FMs in pursuing a creative machine mind, showing that C^2-Eval is an effective lens for examining the evolving landscape of creative AI.
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。
