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

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arxiv 2501.00332 v1 pith:NFG7CKDS submitted 2024-12-31 cs.CL cs.IR

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

Chia-Yuan Chang , Zhimeng Jiang , Vineeth Rakesh , Menghai Pan , Chin-Chia Michael Yeh , Guanchu Wang , Mingzhi Hu , Zhichao Xu
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Yan Zheng Mahashweta Das Na Zou
This is my paper
classification cs.CL cs.IR
keywords documentsfilteringmain-raggenerationretrieval-augmentedaccuracyanswerapproach
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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(LLMs) are becoming essential tools for various natural language processing tasks but often suffer from generating outdated or incorrect information.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.

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