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Hallucination Detection and Reduction in Large Language Models: A Practical Study
Kunal Rajput (Dayananda Sagar College of Engineering), Prof Alamma B H (Dayananda Sagar College of Engineering), Smitha G V (Dayananda Sagar College of Engineering), Hamsa K M (Dayananda Sagar College of Engineering), Madan Kumar Y M (Dayananda Sagar College of Engineering), Mandar Kanbargi (Dayananda Sagar College of Engineering)
Abstract: Large Language Models (LLMs) have become pivotal in generative AI, yet they frequently suffer from “hallucinations”-producing confident but factually incorrect information. This paper introduces the Tri-Layered Hallucination Mitigation Framework (THMF), a multi-stage system designed to identify and reduce fabricated outputs by integrating self-consistency checking, Retrieval-Augmented Generation (RAG), and external knowledge verification. The primary objective of this research is to enhance AI trustworthiness in accuracy-critical domains like healthcare and academic research. Experimental results demonstrate that while a standalone LLM operates at a 68% accuracy rate, the proposed THMF significantly improves performance, achieving a 90% accuracy rate and a low hallucination frequency. These findings suggest that system-level grounding and verification are essential for deploying reliable generative AI without the need for intensive model retraining.