标题： MAIN-RAG：多智能体过滤检索增强生成 显示英文标题

标题： MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation

摘要： 大型语言模型（LLMs）正在成为各种自然语言处理任务的重要工具，但常常会生成过时或错误的信息。检索增强生成（RAG）通过结合外部实时信息检索来使LLM的响应更加可靠，从而解决这个问题。然而，现有的RAG系统经常在检索文档的质量上遇到困难，因为不相关或嘈杂的文档会降低性能，增加计算开销，并损害响应的可靠性。为了解决这个问题，我们提出了多代理过滤检索增强生成（MAIN-RAG），这是一种无需训练的RAG框架，利用多个LLM代理协作过滤和评分检索到的文档。具体而言，MAIN-RAG引入了一种自适应过滤机制，根据评分分布动态调整相关性过滤阈值，有效减少噪声同时保持相关文档的高召回率。所提出的方法利用代理间的一致性来确保稳健的文档选择，而无需额外的训练数据或微调。在四个问答基准上的实验结果表明，MAIN-RAG始终优于传统的RAG方法，在回答准确性方面提高了2-11%，同时减少了不相关检索文档的数量。定量分析进一步表明，我们的方法在响应一致性和回答准确性方面优于基线方法，为基于训练的解决方案提供了一个有竞争力且实用的替代方案。 显示英文摘要

摘要： Large Language Models (LLMs) are becoming essential tools for various natural language processing tasks but often suffer from generating outdated or incorrect information. Retrieval-Augmented Generation (RAG) addresses this issue by incorporating external, real-time information retrieval to ground LLM responses. However, the existing RAG systems frequently struggle with the quality of retrieval documents, as irrelevant or noisy documents degrade performance, increase computational overhead, and undermine response reliability. To tackle this problem, we propose Multi-Agent Filtering Retrieval-Augmented Generation (MAIN-RAG), a training-free RAG framework that leverages multiple LLM agents to collaboratively filter and score retrieved documents. Specifically, MAIN-RAG introduces an adaptive filtering mechanism that dynamically adjusts the relevance filtering threshold based on score distributions, effectively minimizing noise while maintaining high recall of relevant documents. The proposed approach leverages inter-agent consensus to ensure robust document selection without requiring additional training data or fine-tuning. Experimental results across four QA benchmarks demonstrate that MAIN-RAG consistently outperforms traditional RAG approaches, achieving a 2-11% improvement in answer accuracy while reducing the number of irrelevant retrieved documents. Quantitative analysis further reveals that our approach achieves superior response consistency and answer accuracy over baseline methods, offering a competitive and practical alternative to training-based solutions.