大模型对创意测试答案的评价高度一致，可信赖用于自动创意评估。

This paper investigates whether large language models (LLMs) show agreement in assessing creativity in responses to the Alternative Uses Test (AUT). While LLMs are increasingly used to evaluate creative content, previous studies have primarily focused on a single model assessing responses generated by the same model or humans. This paper explores whether LLMs can impartially and accurately evaluate creativity in outputs generated by both themselves and other models. Using an oracle benchmark set of AUT responses, categorized by creativity level (common, creative, and highly creative), we experiment with four state-of-the-art LLMs evaluating these outputs. We test both scoring and ranking methods and employ two evaluation settings (comprehensive and segmented) to examine if LLMs agree on the creativity evaluation of alternative uses. Results reveal high inter-model agreement, with Spearman correlations averaging above 0.7 across models and reaching over 0.77 with respect to the oracle, indicating a high level of agreement and validating the reliability of LLMs in creativity assessment of alternative uses. Notably, models do not favour their own responses, instead they provide similar creativity assessment scores or rankings for alternative uses generated by other models. These findings suggest that LLMs exhibit impartiality and high alignment in creativity evaluation, offering promising implications for their use in automated creativity assessment.