利用大模型评估新颖内容：广告创意案例研究
Leveraging Large Models to Evaluate Novel Content: A Case Study on Advertisement Creativity
AI总结：
针对创意评估的主观性与复杂性问题，该研究将视觉广告创意分解为非典型性和原创性，基于人工标注提出特定任务及基准，评估了SoTA视觉语言模型与人类的对齐程度，为自动创意评估提供了参考。
摘要：
评估创意具有挑战性，即使对人类而言也是如此，这不仅因为其主观性，还因为它涉及复杂的认知过程。受营销领域研究的启发，我们尝试将视觉广告创意分解为非典型性和原创性两个维度。基于这些维度的细粒度人工标注，我们针对这一主观性问题提出了一套特定任务。我们还在提出的基准上评估了最先进（SoTA）视觉语言模型（VLMs）与人类的对齐程度，展示了使用VLMs进行自动创意评估的潜力与挑战。
英文摘要：
Evaluating creativity is challenging, even for humans, not only because of its subjectivity but also because it involves complex cognitive processes. Inspired by work in marketing, we attempt to break down visual advertisement creativity into atypicality and originality. With fine-grained human annotations on these dimensions, we propose a suite of tasks specifically for such a subjective problem. We also evaluate the alignment between state-of-the-art (SoTA) vision language models (VLMs) and humans on our proposed benchmark, demonstrating both the promises and challenges of using VLMs for automatic creativity assessment.
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。
