This research introduces conformal abstention policies that dynamically adapt to uncertainty in large language and vision-language models, enhancing reliability in high-risk scenarios.

• Develops a framework that automatically adjusts abstention thresholds based on task complexity and data distributions
• Enables models to selectively abstain from making predictions when confidence is low
• Provides statistical guarantees for risk management while maintaining high utility
• Offers practical solutions for real-time risk assessment in safety-critical applications

This advancement is particularly valuable for security applications where model reliability is crucial, helping organizations deploy AI systems with greater confidence in their decision-making processes.

Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language Models