Can a Small Model Learn to Look Before It Leaps? Dynamic Learning and Proactive Correction for Hallucination Detection

Researchers propose LEAP, a new framework for detecting AI hallucinations using efficient small models that can dynamically adapt verification strategies. The system uses a teacher-student approach where a powerful model trains smaller ones to detect false outputs, addressing a critical barrier to safe AI deployment in production environments.