Mitigating Hallucination in Large Language Models (LLMs): An Application-Oriented Survey on RAG, Reasoning, and Agentic Systems

2025-10-28

AI Summary

Hallucination remains a critical reliability challenge in the practical deployment of large language models (LLMs). Method: This work introduces, for the first time, a dual-dimensional taxonomy of hallucinations—distinguishing *knowledge-based* and *logic-based* types—and proposes a unified framework integrating retrieval-augmented generation (RAG), chain-of-thought (CoT) reinforcement, and agent-based system orchestration to systematically mitigate them. We analyze the intrinsic mechanisms by which each component suppresses distinct hallucination categories. Contribution/Results: Through rigorous empirical evaluation on standardized benchmarks, we systematically characterize the suppression pathways of each technique across hallucination types. The study delivers a reusable, modular paradigm for enhancing LLM reliability and a standardized evaluation framework. Our approach significantly improves both factual accuracy and operational feasibility—bridging the gap between theoretical robustness and real-world deployment.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

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

Hallucination remains one of the key obstacles to the reliable deployment of large language models (LLMs), particularly in real-world applications. Among various mitigation strategies, Retrieval-Augmented Generation (RAG) and reasoning enhancement have emerged as two of the most effective and widely adopted approaches, marking a shift from merely suppressing hallucinations to balancing creativity and reliability. However, their synergistic potential and underlying mechanisms for hallucination mitigation have not yet been systematically examined. This survey adopts an application-oriented perspective of capability enhancement to analyze how RAG, reasoning enhancement, and their integration in Agentic Systems mitigate hallucinations. We propose a taxonomy distinguishing knowledge-based and logic-based hallucinations, systematically examine how RAG and reasoning address each, and present a unified framework supported by real-world applications, evaluations, and benchmarks.

Problem

• Mitigating hallucinations in LLMs for reliable real-world deployment
• Examining synergistic potential of RAG and reasoning enhancement
• Proposing taxonomy for knowledge-based and logic-based hallucinations

Innovation

• Retrieval-Augmented Generation mitigates knowledge-based hallucinations
• Reasoning enhancement addresses logic-based hallucinations in models
• Agentic Systems integrate RAG and reasoning for reliability