MultiRisk: Controlling AI Behavior with Score Thresholding
Analysis
This paper addresses the critical problem of controlling the behavior of generative AI systems, particularly in real-world applications where multiple risk dimensions need to be managed. The proposed method, MultiRisk, offers a lightweight and efficient approach using test-time filtering with score thresholds. The paper's contribution lies in formalizing the multi-risk control problem, developing two dynamic programming algorithms (MultiRisk-Base and MultiRisk), and providing theoretical guarantees for risk control. The evaluation on a Large Language Model alignment task demonstrates the effectiveness of the algorithm in achieving close-to-target risk levels.
Key Takeaways
- •Proposes MultiRisk, a method for controlling multiple risks in generative AI.
- •Uses test-time filtering with score thresholds for lightweight behavior control.
- •Introduces two dynamic programming algorithms for efficient risk management.
- •Provides theoretical guarantees for risk control.
- •Demonstrates effectiveness on a Large Language Model alignment task.
“The paper introduces two efficient dynamic programming algorithms that leverage this sequential structure.”