The AGSER approach leverages attention patterns within LLMs to detect and reduce hallucinations without requiring additional training or external models.

• Separates input queries into attentive and non-attentive categories based on attention contribution
• Processes each query type separately to compute consistency scores between responses
• Achieves superior performance compared to existing methods, particularly for challenging queries
• Demonstrates effectiveness across multiple LLM architectures including GPT and Llama models

This research addresses a critical security challenge for enterprise LLM deployment by improving the reliability of AI-generated content without additional computational overhead.

Attention-guided Self-reflection for Zero-shot Hallucination Detection in Large Language Models