SelfCheckAgent presents a novel framework utilizing three specialized agents to detect hallucinations in Large Language Models without requiring additional training data.

• Multi-agent approach combines symbolic reasoning, specialized detection, and contextual consistency checks
• Zero-resource methodology works effectively without extensive training datasets
• Enhanced reliability for security-critical applications where false information poses risks
• Cross-domain applicability for detecting misleading content in various contexts

This research addresses critical security concerns by providing mechanisms to identify and flag potentially harmful misinformation generated by AI systems, reducing deployment risks in sensitive environments.

SelfCheckAgent: Zero-Resource Hallucination Detection in Generative Large Language Models