TOBUGraph: Knowledge Graph-Based Retrieval for Enhanced LLM Performance Beyond RAG
Published 6 Nov 2025 · arXiv · Savini Kashmira
Overview
TOBUGraph is a novel retrieval framework that leverages knowledge graphs to enhance the performance of Large Language Models (LLMs) beyond the capabilities of Retrieval-Augmented Generation (RAG). It addresses RAG's limitations by dynamically constructing knowledge graphs from unstructured data, improving retrieval accuracy and reducing hallucinations.
Key Insights
- Improved Retrieval Accuracy: TOBUGraph enhances precision and recall in LLMs by using graph-based retrieval, outperforming multiple RAG implementations.
- Semantic Relationship Capture: It captures deeper semantic relationships, eliminating the need for chunking configurations.
- Real-World Application: Demonstrated effectiveness in TOBU, a personal memory organization and retrieval application.
BFSI Relevance
- Why Relevant: Enhanced retrieval accuracy is crucial for BFSI sectors relying on precise data retrieval for decision-making.
- Primary Sector: Financial Services
- Subsectors: Asset Management, Corporate Banking
- Actionable Implications: BFSI professionals should explore integrating knowledge graph-based retrieval systems to improve data handling and decision-making processes.
researcher peer-reviewed-paper global