Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by retrieving relevant memories from an external database. However, existing RAG methods typically organize all memories in a whole database, potentially limiting focus on crucial memories and introducing noise. In this paper, we introduce a multiple partition paradigm for RAG (called M-RAG), where each database partition serves as a basic unit for RAG execution. Based on this paradigm, we propose a novel framework that leverages LLMs with Multi-Agent Reinforcement Learning to optimize different language generation tasks explicitly. Through comprehensive experiments conducted on seven datasets, spanning three language generation tasks and involving three distinct language model architectures, we confirm that M-RAG consistently outperforms various baseline methods, achieving improvements of 11%, 8%, and 12% for text summarization, machine translation, and dialogue generation, respectively.

翻译：检索增强生成（RAG）通过从外部数据库检索相关记忆来增强大语言模型（LLMs）的性能。然而，现有的RAG方法通常将全部记忆组织在单一数据库中，这可能限制对关键记忆的关注并引入噪声。本文提出一种多分区范式的RAG方法（称为M-RAG），其中每个数据库分区作为RAG执行的基本单元。基于此范式，我们提出一个新颖框架，利用大语言模型结合多智能体强化学习来显式优化不同的语言生成任务。通过在七个数据集上进行全面实验，涵盖三项语言生成任务并涉及三种不同的语言模型架构，我们证实M-RAG始终优于各种基线方法，在文本摘要、机器翻译和对话生成任务上分别实现了11%、8%和12%的性能提升。