Sign up to save your podcasts
Or
Share 【第174期】MMOA-RAG：Multi-Agent RL for Enhanced RAG
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
【第174期】MMOA-RAG：Multi-Agent RL for Enhanced RAG
Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement LearningSummary
The paper introduces MMOA-RAG, a novel approach to improve Retrieval-Augmented Generation (RAG) by framing it as a multi-agent reinforcement learning problem. It addresses the issue of independently optimized RAG components by treating each module (query rewriting, document retrieval, etc.) as an individual agent. MMOA-RAG uses multi-agent reinforcement learning to align each agent's goal with the overarching goal of generating accurate answers. Experiments on question-answering datasets demonstrate that MMOA-RAG outperforms existing methods by jointly optimizing the modules and addressing interdependencies. Ablation studies validate the contribution of each component, supporting MMOA-RAG's adaptability across datasets.
论文介绍了 MMOA-RAG，一种通过将 检索增强生成（RAG） 问题转化为多智能体强化学习问题的新方法。该方法解决了传统 RAG 中各模块（如查询重写、文档检索等）独立优化的问题，将每个模块视为一个独立的智能体。MMOA-RAG 采用多智能体强化学习，使每个智能体的目标与生成准确答案的总体目标对齐。
在问答数据集上的实验表明，MMOA-RAG 通过联合优化各个模块并解决模块之间的相互依赖，超越了现有方法。消融实验验证了每个组件的贡献，进一步支持了 MMOA-RAG 在不同数据集上的适应性。
原文链接：https://arxiv.org/abs/2501.15228
View all episodes
By 任雨山Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement LearningSummary
The paper introduces MMOA-RAG, a novel approach to improve Retrieval-Augmented Generation (RAG) by framing it as a multi-agent reinforcement learning problem. It addresses the issue of independently optimized RAG components by treating each module (query rewriting, document retrieval, etc.) as an individual agent. MMOA-RAG uses multi-agent reinforcement learning to align each agent's goal with the overarching goal of generating accurate answers. Experiments on question-answering datasets demonstrate that MMOA-RAG outperforms existing methods by jointly optimizing the modules and addressing interdependencies. Ablation studies validate the contribution of each component, supporting MMOA-RAG's adaptability across datasets.
论文介绍了 MMOA-RAG，一种通过将 检索增强生成（RAG） 问题转化为多智能体强化学习问题的新方法。该方法解决了传统 RAG 中各模块（如查询重写、文档检索等）独立优化的问题，将每个模块视为一个独立的智能体。MMOA-RAG 采用多智能体强化学习，使每个智能体的目标与生成准确答案的总体目标对齐。
在问答数据集上的实验表明，MMOA-RAG 通过联合优化各个模块并解决模块之间的相互依赖，超越了现有方法。消融实验验证了每个组件的贡献，进一步支持了 MMOA-RAG 在不同数据集上的适应性。
原文链接：https://arxiv.org/abs/2501.15228