RAG-KG-IL: A Multi-Agent Hybrid Framework for Reducing Hallucinations and Enhancing LLM Reasoning through RAG and Incremental Knowledge Graph Learning Integration

2025-03-14

AI Summary

This paper addresses three critical challenges in deploying large language models (LLMs) for mission-critical applications: frequent hallucination, weak structured-data reasoning capability, and poor adaptability to dynamic knowledge evolution. To tackle these, we propose RAG-KG-IL—a multi-agent collaborative framework integrating retrieval-augmented generation (RAG), a retraining-free, dynamically evolving knowledge graph (KG), and incremental learning (IL). The framework enables real-time KG updating, autonomous multi-agent collaborative reasoning, and seamless integration with a structured reasoning engine. Its core innovation is the first-of-its-kind coupling mechanism among RAG, KG, and IL, substantially enhancing reasoning interpretability and domain adaptability. Empirical evaluation in the healthcare domain demonstrates a 42% reduction in hallucination rate, a 37% improvement in answer completeness, and superior reasoning accuracy compared to GPT-4o and state-of-the-art RAG baselines.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Knowledge Representation Languages
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

This paper presents RAG-KG-IL, a novel multi-agent hybrid framework designed to enhance the reasoning capabilities of Large Language Models (LLMs) by integrating Retrieval-Augmented Generation (RAG) and Knowledge Graphs (KGs) with an Incremental Learning (IL) approach. Despite recent advancements, LLMs still face significant challenges in reasoning with structured data, handling dynamic knowledge evolution, and mitigating hallucinations, particularly in mission-critical domains. Our proposed RAG-KG-IL framework addresses these limitations by employing a multi-agent architecture that enables continuous knowledge updates, integrates structured knowledge, and incorporates autonomous agents for enhanced explainability and reasoning. The framework utilizes RAG to ensure the generated responses are grounded in verifiable information, while KGs provide structured domain knowledge for improved consistency and depth of understanding. The Incremental Learning approach allows for dynamic updates to the knowledge base without full retraining, significantly reducing computational overhead and improving the model's adaptability. We evaluate the framework using real-world case studies involving health-related queries, comparing it to state-of-the-art models like GPT-4o and a RAG-only baseline. Experimental results demonstrate that our approach significantly reduces hallucination rates and improves answer completeness and reasoning accuracy. The results underscore the potential of combining RAG, KGs, and multi-agent systems to create intelligent, adaptable systems capable of real-time knowledge integration and reasoning in complex domains.

Problem

• Enhance LLM reasoning with RAG and Knowledge Graphs.
• Reduce hallucinations in mission-critical domains.
• Enable dynamic knowledge updates without full retraining.

Innovation

• Integrates RAG and Knowledge Graphs for reasoning
• Uses Incremental Learning for dynamic knowledge updates
• Multi-agent system enhances explainability and adaptability