Know What You Don't Know: Uncertainty Calibration of Process Reward Models

Overview

The paper discusses a calibration approach for process reward models (PRMs) used in large language models (LLMs). It addresses the issue of PRMs overestimating success probabilities, especially with smaller LLMs.

Key Insights

• Calibration Method: The authors propose a quantile regression-based calibration method to align PRM outputs with true success probabilities.
• Instance-Adaptive Scaling (IAS): The calibrated PRMs support an IAS framework that dynamically adjusts compute budgets based on success likelihood.
• Performance: Experiments show reduced calibration error and inference costs while maintaining accuracy.

BFSI Relevance

• Why Relevant: Accurate PRMs can optimize computational resources, crucial for cost management in BFSI sectors using AI for decision-making.
• Primary Sector: Financial Services
• Subsectors: Asset Management, Risk Management
• Actionable Implications: BFSI professionals should consider adopting calibrated PRMs to enhance AI efficiency and reduce operational costs.