AgentKGV: Agentic LLM-RAG Framework with Two-Stage Training for the Fact Verification of Knowledge Graphs
2026-07-10 • Computation and Language
Computation and Language
AI summaryⓘ
The authors created AgentKGV, a system that helps check if facts in large knowledge graphs (KGs) are correct by using smart question-asking methods with large language models. They made the system better by training smaller models to think more like bigger ones and by teaching it to ask fewer unnecessary questions. Their approach improved accuracy and made the fact-checking process faster on a challenging test set. This helps verify KG facts more reliably and efficiently at large scale.
Knowledge GraphFact VerificationLarge Language ModelsRetrieval-Augmented GenerationQuery RewritingTwo-Stage TrainingDistillationGRPOT-REx BenchmarkInformation Retrieval
Authors
Yumin Heo, Hyeon-gu Lee, Sumin Seo, Youngjoong Ko
Abstract
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.