May 2025
Intermediate to advanced
382 pages
10h 33m
Chinese
Content preview from 图解大模型 : 生成式AI 原理与实战
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218
｜
第
8
章
答案相关性（answer relevance）
答案与提问主题的契合度。
Ragas
官方文档详细阐述了各项指标的计算公式。
8.4
小结
本章系统探讨了语言模型在搜索系统中的创新应用。
•
稠密检索
：基于文本嵌入相似性的检索机制，通过将搜索查询向量化，匹配最相近的文
档嵌入。
•
重排器
：以
monoBERT
为代表的系统，通过评估查询与候选文档的相关性分数实现结
果排序优化。
•
RAG
：在搜索流程末端部署生成式
LLM
，基于检索所得文档生成附带引证的回答。
我们还介绍了一种可行的搜索系统评估方法。均值平均精确率允许我们为搜索系统评分，
从而基于一组测试查询及已知的相关性数据进行系统间的比较。值得注意的是，
RAG
系统
的评估需要涵盖多个维度，包括忠实度、流畅性等指标，这些维度既可以通过人工评估，
也可以借助
LLM-as-a-judge
进行量化分析。
在下一章中，我们将深入探讨语言模型的多模态扩展技术，使其不仅能够处理文本信息，
还具备理解视觉内容的能力。
图解大模型：生成式AI原理与实战（全）.indd 218图解大模型：生成式AI原理与实战（全）.indd 218 2025/4/24 9:21:432025/4/24 9:21:43
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.