多智能体框架提升带引用问答的准确与可信度
RAGentA: Multi-Agent Retrieval-Augmented Generation for Attributed Question Answering
- 采用多智能体迭代过滤文档并生成带引用的答案
- 混合检索使召回率提升12.5%，正确率和可信度显著提高
- 适合需要高可信度回答的科研或专业场景
我们提出RAGentA，一种用于属性化问答（Attributed QA）的多智能体检索增强生成框架。为实现可信答案生成，RAGentA聚焦于提升答案正确性（涵盖问题范围与相关性）与忠实性（答案基于检索文档的程度）。该框架采用多智能体架构，通过迭代过滤检索文档、生成带内联引用的答案，并动态优化完整性。核心是融合稀疏与密集检索的混合策略，在细粒度网页索引构建的合成数据集上，相比最优单模型检索方法，召回率@20提升12.5%。在属性化问答任务中，优于标准RAG基线，正确率提升1.09%，忠实性提升10.72%。结果表明，多智能体架构与混合检索策略能有效提升大语言模型在可信问答中的表现。
原文摘要 · Abstract (English)
We present RAGentA, a multi-agent retrieval-augmented generation (RAG) framework for attributed question answering (QA) with large language models (LLMs). With the goal of trustworthy answer generation, RAGentA focuses on optimizing answer correctness, defined by coverage and relevance to the question and faithfulness, which measures the extent to which answers are grounded in retrieved documents. RAGentA uses a multi-agent architecture that iteratively filters retrieved documents, generates attributed answers with in-line citations, and verifies completeness through dynamic refinement. Central to the framework is a hybrid retrieval strategy that combines sparse and dense methods, improving Recall@20 by 12.5% compared to the best single retrieval model, resulting in more correct and well-supported answers. Evaluated on a synthetic QA dataset derived from the FineWeb index, RAGentA outperforms standard RAG baselines, achieving gains of 1.09% in correctness and 10.72% in faithfulness. These results demonstrate the effectiveness of our multi-agent RAG architecture and hybrid retrieval strategy in advancing trustworthy QA with LLMs.
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