用大模型评估广告创意，拆解为罕见性和原创性。
Leveraging Large Models to Evaluate Novel Content: A Case Study on Advertisement Creativity
- 将广告创意拆分为罕见性与原创性，设计细粒度评估任务
- 发现当前视觉语言模型在创意评估上与人类判断存在差距
- 适合对生成内容质量评估感兴趣的AI研究者
评估创意具有挑战性，不仅因主观性强，还涉及复杂的认知过程。受营销学启发，我们将视觉广告创意分解为罕见性与原创性。基于细粒度的人类标注，我们提出了针对此类主观问题的一套评估任务。同时，我们在该基准上评估了当前最先进的视觉语言模型（VLMs）与人类判断的对齐程度，揭示了使用VLM进行自动创意评估的潜力与局限。
原文摘要 · Abstract (English)
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.
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