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

Understand

Autonomous systems are soon to be ubiquitous, spanning manufacturing, agriculture, healthcare, entertainment, and other industries.

• Most of these systems are developed with modular sub-components for decision-making, planning, and control that may be hand-engineered or learning-based.
• While these approaches perform well under the situations they were specifically designed for, they can perform especially poorly in out-of-distribution scenarios that will undoubtedly arise at test-time.
• The rise of foundation models trained on multiple tasks with impressively large datasets has led researchers to believe that these models may provide "common sense" reasoning that existing planners are missing, bridging the gap between algorithm development and deployment.