W-RAG: Source-Aware Retrieval for Enterprise Document Generation from Heterogeneous Knowledge Bases
What happened
A new framework, W-RAG, addresses the challenge of Retrieval-Augmented Generation (RAG) systems producing unbalanced context from heterogeneous knowledge bases by implementing source-aware retrieval and ontology-guided topic extraction. This approach aims to overcome limitations where traditional RAG often produces context dominated by a subset of sources.
Why it matters
AI Engineers building RAG systems for enterprise document generation should move beyond global similarity ranking and implement source-aware retrieval with ontology-guided topic extraction and local ranking within each knowledge base to ensure balanced and relevant context.
Topics
- Retrieval-Augmented Generation
- Enterprise Document Generation
- Heterogeneous Knowledge Bases
- Ontology-Guided Retrieval
Articles in this trend
- W-RAG: Source-Aware Retrieval for Enterprise Document Generation from Heterogeneous Knowledge Bases — cs.SE updates on arXiv.org
- RAG Collapse: LLM Responses Collapse When Retrieved Documents Are Self-Authored — cs.CL updates on arXiv.org
- 10 Positions for Enterprise RAG That Mainstream Tutorials Get Wrong — Towards Data Science
- Part 1: Retrieval-Augmented Generation (RAG) from First Principles: What, Why, and How It Evolved — Towards AI - Medium
- Neo4j Needs Just 1 Hop to Answer What Your LLM Was Guessing — Artificial Intelligence on Medium
- RAG Security Explained: From AI Architecture to AI VAPT Attack Surface — LLM on Medium