用大模型生成播客预览，效果更好且效率提升5倍
Transforming Podcast Preview Generation: From Expert Models to LLM-Based Systems
- 用大模型替代传统专家模型生成播客预览
- 线上测试用户参与度提升4.6%，处理效率提高5倍
- 适合需要高效内容摘要的平台和产品团队
发现和评估长篇对话类内容（如视频和播客）对用户而言是一项重大挑战，因其需投入大量时间。预览通过提供关键片段，帮助用户更快速地做出选择。本文提出一种基于大模型的播客节目预览生成方法，并在真实场景中规模化部署，服务了数十万次播客预览。离线评估与在线A/B测试表明，大模型生成的预览持续优于基于多种机器学习专家模型的强基线，显著减少对精细特征工程的依赖。离线结果显示理解性、上下文清晰度和兴趣度均有明显提升；在线测试中，用户对预览内容的参与度提升了4.6%，处理效率提高5倍，相较传统特征工程专家模型更具流畅性与性能优势。
原文摘要 · Abstract (English)
Discovering and evaluating long-form talk content such as videos and podcasts poses a significant challenge for users, as it requires a considerable time investment. Previews offer a practical solution by providing concise snippets that showcase key moments of the content, enabling users to make more informed and confident choices. We propose an LLM-based approach for generating podcast episode previews and deploy the solution at scale, serving hundreds of thousands of podcast previews in a real-world application. Comprehensive offline evaluations and online A/B testing demonstrate that LLM-generated previews consistently outperform a strong baseline built on top of various ML expert models, showcasing a significant reduction in the need for meticulous feature engineering. The offline results indicate notable enhancements in understandability, contextual clarity, and interest level, and the online A/B test shows a 4.6% increase in user engagement with preview content, along with a 5x boost in processing efficiency, offering a more streamlined and performant solution compared to the strong baseline of feature-engineered expert models.
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