LLM Confidence Calibration via Temperature Scaling and Polynomial Regression
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Solution Overview
Problem
Current technologies fail to effectively improve both calibration and selective prediction accuracy for Large Language Models (LLMs) in a computationally inexpensive manner, leading to inaccurate data extraction and understanding, and existing solutions either degrade calibration quality or selective prediction accuracy.
Innovation Solution
The Post-Hoc Calibration (PHC) Solution Set combines temperature scaling, softmax denominator top-k probabilities selection, and polynomial regression to recalibrate confidence scores, ensuring both improved calibration and selective prediction accuracy without significant computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If temperature scaling is applied to improve confidence score calibration, then calibration quality improves, but selective prediction accuracy may degrade
Solution Approach 1:
The patent combines temperature scaling with polynomial regression calibration to create a hybrid approach that leverages the computational efficiency of temperature scaling while using polynomial regression to correct its calibration errors, thereby maintaining both calibration quality and selective prediction accuracy
Solution Approach 2:
The patent modifies the temperature scaling approach by introducing polynomial regression as an additional calibration layer that adjusts the temperature-scaled probabilities through parameter optimization, resolving the trade-off between calibration and prediction accuracy
2Measurement precision
If complex calibration methods are used to improve confidence score accuracy, then calibration quality improves, but computational overhead increases
Solution Approach 1:
The patent segments the calibration process into two distinct stages: temperature scaling for initial calibration and polynomial regression for fine-tuning, allowing each component to be optimized independently and reducing overall computational burden
Solution Approach 2:
The patent employs polynomial regression as a computationally inexpensive post-processing step that provides significant calibration improvement without the heavy computational cost of alternative methods, effectively serving as a lightweight calibration solution
3Measurement precision
If existing calibration techniques are applied to LLM outputs, then confidence scores are adjusted, but both calibration quality and selective prediction accuracy cannot be simultaneously improved
Solution Approach 1:
The patent implements a feedback mechanism where polynomial regression learns from the discrepancies between temperature-scaled confidence scores and actual prediction outcomes, continuously optimizing the calibration function to improve both calibration quality and selective prediction accuracy simultaneously
Data Source
AI summary
Examples provide a large language model confidence scoring post-calibration based on a combination of temperature scaling, softmax denominator top-k probabilities selection, and polynomial regression. A secure machine learning system receives results generated by a machine learning (ML) model, the results including at least one confidence score. The secure ML system identifies at least one challenge in accuracy of the results generated by the ML model configured to perform document processing and understanding. The secure machine learning system implements confidence scoring recalibration to address at least one challenge, the confidence scoring recalibration including functionality to assess reliability of the results generated by the ML model, and applies post-processing calibration to the at least one confidence score generated by the confidence scoring recalibration to enhance performance of the ML model, the post-processing calibration including adjusting the at least one confidence score generated by the confidence scoring recalibration.