Abstract
In this study, we tackle industry challenges in video content classification by exploring and optimizing GPT-based models for zero-shot classification across seven critical categories of video quality. We contribute a novel approach to improving GPT's performance through prompt optimization and policy refinement, demonstrating that simplifying complex policies significantly reduces false negatives. Additionally, we introduce a new decomposition-aggregation-based prompt engineering technique, which outperforms traditional single-prompt methods. These experiments, conducted on real industry problems, show that thoughtful prompt design can substantially enhance GPT's performance without additional finetuning, offering an effective and scalable solution for improving video classification systems across various domains in industry.
Abstract (translated)
在这项研究中，我们通过探索和优化基于GPT的模型来解决视频内容分类中的行业挑战，在七个关键的视频质量类别上实现了零样本分类。我们提出了一种新颖的方法，通过优化提示和细化策略来提升GPT的表现，证明了简化复杂策略可以显著减少假阴性错误。此外，我们还引入了一种基于分解-聚合的新颖提示工程技术，这种技术超越了传统的单一提示方法。这些实验是在实际行业问题上进行的，结果显示，精心设计的提示可以在不增加额外微调的情况下大幅提升GPT的表现，为改善各个领域的视频分类系统提供了一个有效且可扩展的解决方案。
URL
https://arxiv.org/abs/2502.09573
