UltRAG: a Universal Simple Scalable Recipe for Knowledge Graph RAG

2026-01-28

arXiv.org

AI Summary

This study addresses the challenges of hallucination in large language models (LLMs) and multi-hop reasoning retrieval over knowledge graphs (KGs) by proposing a training-free, generalizable KG-RAG framework. The method achieves efficient knowledge graph question answering through the synergistic integration of LLM-based query generation, an inductive neural executor, and an arbitration mechanism. Its core innovation lies in introducing the first training-agnostic paradigm capable of scaling to Wikidata-level massive knowledge graphs without requiring model retraining. Experimental results demonstrate that the proposed approach attains state-of-the-art performance on KGQA benchmarks while significantly reducing inference costs and enhancing factual accuracy.

Abstract

Large language models (LLMs) frequently generate confident yet factually incorrect content when used for language generation (a phenomenon often known as hallucination). Retrieval augmented generation (RAG) tries to reduce factual errors by identifying information in a knowledge corpus and putting it in the context window of the model. While this approach is well-established for document-structured data, it is non-trivial to adapt it for Knowledge Graphs (KGs), especially for queries that require multi-node/multi-hop reasoning on graphs. We introduce ULTRAG, a general framework for retrieving information from Knowledge Graphs that shifts away from classical RAG. By endowing LLMs with off-the-shelf neural query executing modules, we highlight how readily available language models can achieve state-of-the-art results on Knowledge Graph Question Answering (KGQA) tasks without any retraining of the LLM or executor involved. In our experiments, ULTRAG achieves better performance when compared to state-of-the-art KG-RAG solutions, and it enables language models to interface with Wikidata-scale graphs (116M entities, 1.6B relations) at comparable or lower costs.

Problem

• Hallucination
• Retrieval Augmented Generation
• Knowledge Graphs
• Multi-hop Reasoning
• Knowledge Graph Question Answering

Innovation

• Knowledge Graph RAG
• Training-free
• Fully Inductive Neural Query Executor
• Scalability
• KGQA