超越发散性创造：基于人类的大型语言模型创造力评估
Beyond Divergent Creativity: A Human-Based Evaluation of Creativity in Large Language Models
AI总结：
本文提出CDAT任务，基于人类创造力理论，评估LLMs在新颖性与适当性之间的平衡，发现较小模型更具创造力，而高级模型更注重适当性。
摘要：
大型语言模型（LLMs）越来越多地用于口头创造性任务。然而，以前对LLMs创造力能力的评估仍然缺乏对人类创造力理论的坚实基础，因此难以解释。广泛使用的发散关联任务（DAT）侧重于新颖性，忽略了适当性，这是创造力的核心组成部分。我们对一系列最先进的LLMs进行了DAT评估，显示其在该任务上的得分低于两个不具有任何创造力能力的基线模型，从而质疑了其对模型评估的有效性。基于人类创造力理论，该理论将创造力定义为新颖性与适当性的结合，我们引入了条件发散关联任务（CDAT）。CDAT在适当性条件下评估新颖性，比DAT更好地区分噪声与创造力，同时保持简单和客观。在CDAT下，较小的模型家族往往表现出更多的创造力，而高级家族则更倾向于适当性，但新颖性较低。我们假设训练和对齐过程可能使模型沿着这一前沿移动，使输出更加适当但缺乏创造力。我们发布了数据集和代码。
英文摘要：
Large language models (LLMs) are increasingly used in verbal creative tasks. However, previous assessments of the creative capabilities of LLMs remain weakly grounded in human creativity theory and are thus hard to interpret. The widely used Divergent Association Task (DAT) focuses on novelty, ignoring appropriateness, a core component of creativity. We evaluate a range of state-of-the-art LLMs on DAT and show that their scores on the task are lower than those of two baselines that do not possess any creative abilities, undermining its validity for model evaluation. Grounded in human creativity theory, which defines creativity as the combination of novelty and appropriateness, we introduce Conditional Divergent Association Task (CDAT). CDAT evaluates novelty conditional on contextual appropriateness, separating noise from creativity better than DAT, while remaining simple and objective. Under CDAT, smaller model families often show the most creativity, whereas advanced families favor appropriateness at lower novelty. We hypothesize that training and alignment likely shift models along this frontier, making outputs more appropriate but less creative. We release the dataset and code.
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。
