Project Overview

This project focuses on developing comprehensive methods to detect and evaluate hallucination in large language models. Hallucination, where models generate false or misleading information, is a critical challenge in NLP that affects the reliability of AI systems.

Key Contributions

• Survey of Evaluation Methods: Comprehensive review of existing hallucination evaluation techniques
• Novel Detection Algorithms: Development of new methods for identifying hallucinated content
• Evaluation Framework: Creation of standardized evaluation protocols for hallucination detection

Technical Approach

The project employs multiple approaches:

• Statistical Analysis: Using statistical methods to identify inconsistencies
• Semantic Verification: Comparing generated content against source materials
• Cross-Reference Checking: Verifying claims against knowledge bases

Results

• Published survey paper on arXiv with significant citations
• Developed evaluation toolkit for the research community
• Contributed to understanding of hallucination patterns in different model types

Technologies Used

• Python, PyTorch, Transformers
• Natural Language Processing libraries
• Statistical analysis tools

Project Status

Ongoing - Currently expanding to include more sophisticated detection methods and evaluation metrics.