用强化学习教RAG模型精准搜索，提升答案可信度
Careful Queries, Credible Results: Teaching RAG Models Advanced Web Search Tools with Reinforcement Learning
- 通过强化学习优化查询生成与结果过滤，限制来源提升准确性
- 在跨域和本域测试中均超越现有RAG方法，显著提升检索精度
- 适合需要高可信度问答的场景，如医疗、金融领域应用
检索增强生成（RAG）通过引入外部知识提升大语言模型性能，但真实网络环境存在两大挑战：一是广泛存在的虚假信息导致检索结果不可靠；二是网络工具未被充分使用，若合理运用可提高查询精度并降低噪声。为此，我们提出WebFilter框架，通过生成受源限制的查询并过滤不可信内容，结合行为与结果驱动的奖励机制，同时优化查询策略与检索效果。大量实验表明，WebFilter在跨域与本域基准上均优于现有RAG方法，显著提升答案质量与检索精度。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating up-to-date external knowledge, yet real-world web environments present unique challenges. These limitations manifest as two key challenges: pervasive misinformation in the web environment, which introduces unreliable or misleading content that can degrade retrieval accuracy, and the underutilization of web tools, which, if effectively employed, could enhance query precision and help mitigate this noise, ultimately improving the retrieval results in RAG systems. To address these issues, we propose WebFilter, a novel RAG framework that generates source-restricted queries and filters out unreliable content. This approach combines a retrieval filtering mechanism with a behavior- and outcome-driven reward strategy, optimizing both query formulation and retrieval outcomes. Extensive experiments demonstrate that WebFilter improves answer quality and retrieval precision, outperforming existing RAG methods on both in-domain and out-of-domain benchmarks.
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