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所在平台: Udemy |
课程主页: https://www.udemy.com/course/comprehensive-retrieval-augmented-generation-rag-test/
课程评论：没有评论
**课程名称：** RAG（检索增强生成）综合测试 **课程概述：** 本课程深入探讨了检索增强生成（RAG）技术，该技术结合了检索和生成模型的优势，在人工智能时代开启了自然语言处理（NLP）的新篇章。通过融合检索的精准性和生成模型的创造性，RAG能够生成高度准确且富有上下文关联性的文本。 **课程目标：** 完成本课程后，学员将能够： * **理解RAG基础：** 深入掌握RAG的原理和架构，包括检索器（retriever）和生成器（generator）组件的作用。 * **学习密集文档检索（DPR）：** 了解DPR及其在利用神经嵌入提升检索过程中的重要性。 * **掌握参数高效微调（PEFT）：** 学习通过训练少量参数（如LoRA）来高效微调大型语言模型（LLMs）的技巧。 * **整合检索与生成：** 掌握如何有效地将检索到的信息融入生成模型，以产生更准确、更相关的响应。 * **应用高级预处理技术：** 理解为优化检索和生成组件所需的预处理步骤。 * **评估与优化RAG模型：** 掌握使用精确匹配（EM）和F1分数等指标评估RAG模型性能的技能，并学习针对特定任务优化模型的方法。
In the era of advanced artificial intelligence, the integration of retrieval mechanisms with generative models has opened new frontiers in natural language processing (NLP). This course, " Retrieval-Augmented Generation (RAG) for Fine-Tuning Large Language Models," provides a comprehensive exploration of the cutting-edge methodology that combines the strengths of retrieval-based and generative approaches to produce highly accurate and contextually relevant text.By the end of this course, participants will:Understand the Fundamentals of RAG: Gain a deep understanding of the principles and architecture of Retrieval-Augmented Generation, including the roles of the retriever and generator components.Learn Dense Passage Retrieval (DPR): Explore Dense Passage Retrieval and its importance in enhancing the retrieval process using neural embeddings.Master Parameter-Efficient Fine-Tuning (PEFT): Discover techniques for fine-tuning large language models efficiently by training a small subset of parameters, such as LoRA (Low-Rank Adaptation).Integrate Retrieval with Generation: Learn how to effectively integrate retrieved information into generative models to produce more accurate and relevant responses.Apply Advanced Preprocessing Techniques: Understand the preprocessing steps necessary for optimizing both retrieval and generation components.Evaluate and Optimize RAG Models: Develop skills to evaluate the performance of RAG models using metrics like Exact Match (EM) and F1 score, and learn strategies to optimize these models for specific tasks.
