优化学术领域RAG系统，提升检索生成效果
Towards Optimizing a Retrieval Augmented Generation using Large Language Model on Academic Data
- 融合多查询、父子检索等四种优化策略增强学术数据检索
- 多查询策略使检索阶段性能显著提升
- 适合研究者与教育机构优化智能问答系统
随着越来越多组织将检索增强生成（RAG）纳入实际应用，本文针对特定领域数据评估RAG，并测试多种优化技术在学术场景下的表现。通过引入多查询、父子检索器、集成检索器和上下文学习四种优化方法，提升在大型工科院校不同专业领域的数据检索能力。提出一种新型评估方式——RAG混淆矩阵，用于衡量不同配置在框架中的有效性。实验对比了开源模型（如Llama2、Mistral）与闭源模型（GPT-3.5、GPT-4）的适用性，结果表明在检索阶段加入多查询策略能带来显著性能提升。
原文摘要 · Abstract (English)
Given the growing trend of many organizations integrating Retrieval Augmented Generation (RAG) into their operations, we assess RAG on domain-specific data and test state-of-the-art models across various optimization techniques. We incorporate four optimizations; Multi-Query, Child-Parent-Retriever, Ensemble Retriever, and In-Context-Learning, to enhance the functionality and performance in the academic domain. We focus on data retrieval, specifically targeting various study programs at a large technical university. We additionally introduce a novel evaluation approach, the RAG Confusion Matrix designed to assess the effectiveness of various configurations within the RAG framework. By exploring the integration of both open-source (e.g., Llama2, Mistral) and closed-source (GPT-3.5 and GPT-4) Large Language Models, we offer valuable insights into the application and optimization of RAG frameworks in domain-specific contexts. Our experiments show a significant performance increase when including multi-query in the retrieval phase.
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