LLM Hallucination Detection

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LLM Hallucination Detection

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Overview
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Detail Description

1. Abstract

LLM Hallucination Detection is a Generative AI-based system designed to identify incorrect, unsupported, or fabricated information generated by Large Language Models (LLMs). The system analyzes AI-generated responses, compares claims against trusted documents or reference sources, and assigns confidence or verification scores. It helps improve the reliability, factual consistency, and trustworthiness of AI-generated content.

2. Objectives

Detect factual errors in LLM-generated responses.
Verify generated claims against trusted reference documents.
Identify unsupported statements and misleading information.
Generate confidence scores for individual claims.
Provide evidence-based verification results.
Improve the reliability of Generative AI applications.

3. Existing System

Traditional AI applications may display LLM-generated answers without systematically checking whether the information is factually supported.

Disadvantages

Incorrect information may be presented confidently.
Unsupported claims can be difficult to identify.
Manual fact-checking takes time.
Source verification may be inconsistent.
Users may over-rely on AI-generated responses.

4. Proposed System

The proposed system combines LLMs, NLP, and evidence retrieval to evaluate generated answers against trusted information sources.

Key features:

Input interface for AI-generated responses.
Automatic claim extraction and decomposition.
Evidence retrieval from trusted documents.
Claim-level factual consistency analysis.
Classification as supported, contradicted, or unverifiable.
Confidence and evidence scores.
Highlighting of potentially hallucinated statements.
Source references for verification.
Dashboard displaying verification results.

5. Implementation Procedure

Collect reference documents and sample AI-generated responses.
Build a user interface for submitting text or answers.
Split responses into individual factual claims.
Retrieve supporting evidence from trusted documents using semantic search.
Generate embeddings and store reference passages in FAISS or ChromaDB.
Compare each claim with the retrieved evidence using an NLI model or LLM-based verifier.
Classify claims as supported, contradicted, or insufficiently supported.
Calculate verification scores using evidence relevance and model outputs.
Display flagged claims, supporting evidence, and source references.
Evaluate the system using labeled test datasets and deploy the application.

6. Software Requirements

Programming Language: Python
Frontend: Streamlit or React.js
Backend: Flask or FastAPI
LLM: OpenAI API or a compatible model
NLP Libraries: Transformers and spaCy
Verification Model: Natural Language Inference (NLI) model
Embedding Model: Sentence Transformers
Vector Database: FAISS or ChromaDB
Database: SQLite or PostgreSQL
Development Environment: VS Code
Operating System: Windows or Linux

7. Hardware Requirements

Processor: Intel Core i3 or equivalent
RAM: Minimum 8 GB; 16 GB recommended
Storage: At least 10 GB free space
Internet: Required for hosted AI APIs
GPU: Optional for locally running larger verification models

8. Advantages of the Project

Identifies potentially fabricated information.
Improves trust in AI-generated responses.
Supports claim-level fact verification.
Provides evidence to help users review flagged claims.
Reduces manual fact-checking effort.
Can be integrated into chatbots and RAG applications.
Supports more transparent and accountable AI workflows.

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