This research tackles the critical challenge of identifying when foundation models generate false information in autonomous systems across industries.

Key Insights:

• Proposes flexible frameworks for defining and detecting hallucinations in decision-making systems
• Reviews state-of-the-art detection methods across multiple domains
• Addresses the heightened risks of out-of-distribution scenarios in autonomous operations
• Offers practical approaches for enhancing reliability in AI-powered systems

Security Implications: Hallucination detection is essential for securing autonomous systems against potentially dangerous decisions based on fabricated information. By implementing these detection methods, organizations can significantly reduce safety risks and build more trustworthy AI systems for critical applications.

Hallucination Detection in Foundation Models for Decision-Making: A Flexible Definition and Review of the State of the Art