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.

通过引入多个分区范例（称为 M-RAG），每个数据库分区作为 RAG 执行的基本单位，本文在 Retrieval-Augmented Generation（RAG）的基础上提出了一个新的框架，利用多智能体强化学习优化不同的语言生成任务。通过在七个数据集上进行的综合实验，涵盖三个语言生成任务和三种不同的语言模型架构，我们证实了 M-RAG 相对于各种基准方法的持续优势，分别在文本摘要、机器翻译和对话生成方面取得了 11％、8％和 12％的改进。