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Volume 12, Issue 3 (March 2026)

Hallucination Detection System For Large Language Models (llms) Using Genai

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Volume 12 Issue 07

July 2026

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Author(s)

Keerthi .K Jayashree.S Dharani.R Archana.P Mrs.J. jenila

Abstract

Large Language Models (LLMs) Often Generate Plausible Yet Incorrect Information, Known As Hallucinations. This Paper Proposes A Real-time Hallucination Detection System That Evaluates The Reliability Of LLM Outputs. The System Combines Evidence Retrieval From Trusted Sources, Semantic Similarity Using Sentence Embeddings, And Self-consistency Checks Across Multiple Responses. A Unified Decision Module Classifies Outputs As Factual Or Hallucinated. Implemented As A Streamlit Web Application, The System Provides An Intuitive Interface For Evaluating Responses. This Approach Enhances Transparency, Reliability, And Trust In AI-generated Content For Research And Professional Use.

Keywords

Large Language Models (LLMs) Hallucination Detection Sentence Embeddings Evidence Retrieval Self- Consistency Fact Verification.

Paper ID

IJSARTV12I3104813

Publication Date

March 30, 2026

Research Area

Artificial Intelligence

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