This research evaluates how different retrieval methods reduce hallucinations in LLMs by grounding responses in factual information.

• Compares three retrieval approaches: sparse (keyword-based), dense (semantic), and hybrid methods
• Evaluates how retriever effectiveness correlates with hallucination reduction
• Provides framework for selecting optimal retrieval strategies for specific applications
• Demonstrates how proper knowledge retrieval enhances both accuracy and security of AI systems

Security Impact: By reducing false information generation, these techniques help prevent potential security threats from misinformation, establish greater trust in AI systems, and ensure reliability in critical applications.

Hybrid Retrieval for Hallucination Mitigation in Large Language Models: A Comparative Analysis