用智能体框架提升知识图谱事实验证的准确率与效率
AgentKGV: Agentic LLM-RAG Framework with Two-Stage Training for the Fact Verification of Knowledge Graphs
- 设计动态路由与迭代查询重写机制，解决文档检索中的表述不匹配问题
- 在T-REx数据集上，相比单轮RAG，宏平均F1提升5.5个百分点，两阶段训练再增9.4个百分点
- 通过策略优化减少冗余检索，平均调用次数从3.24降至1.63，适合工业级部署
知识图谱常从大规模语料自动构建，但因数据噪声和抽取失败，不可避免存在事实错误，实现工业级可靠验证仍是关键挑战。为此，我们提出AgentKGV——一种面向知识图谱事实验证的智能体式大模型-RAG框架，融合动态路由与迭代查询重写机制，有效应对文档级检索中的表面形式不匹配问题。为提升框架在工业部署中的准确性与成本效益，进一步引入两阶段训练策略：基于逐轮蒸馏的SFT将大模型推理能力迁移至小模型，保障查询重写与推理稳定性；基于轨迹的GRPO优化搜索策略，降低大规模场景下的冗余检索。在开放域T-REx基准的长尾谓词划分上，该框架相较单轮RAG的宏平均F1提升5.5%pp，两阶段训练使提升幅度达9.4%pp。GRPO还将平均搜索调用次数从3.24降至1.63，且未降低准确性。
原文摘要 · Abstract (English)
Knowledge graphs (KGs) are often automatically constructed from large-scale corpora, but they inevitably contain factual errors due to noisy sources and extraction failures, and verifying them reliably at industrial scale remains a critical challenge. To address this, we propose AgentKGV, the Agentic LLM-RAG framework for KG fact Verification, that integrates dynamic routing and iterative query rewriting, which handles surface-form mismatch in document-level retrieval. To make this framework more accurate and cost-efficient for industrial deployment, we further introduce a two-stage training strategy: turn-level distillation-based SFT that transfers reasoning ability from a large teacher model into a small model for stable query rewriting and reasoning, and trajectory-level GRPO that optimizes the search policy to reduce unnecessary retrieval at scale. On the long-tail-predicate split of the open-domain T-REx benchmark, our framework improves macro-F1 over single-turn RAG by 5.5 \%p, and two-stage training does it further by 9.4 \%p. GRPO also cuts the average number of search calls from 3.24 to 1.63 without lowering accuracy.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。