Dynamic Confidence Thresholding for Question Answering
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Solution Overview
Problem
Conventional question answering systems rely on static confidence thresholds, which can lead to suboptimal performance as they do not adapt to changing content, usage patterns, or user preferences, resulting in either discarding correct answers or providing numerous incorrect ones.
Innovation Solution
A dynamic confidence thresholding method that adjusts the threshold based on user-defined target answering frequencies, incrementing or decrementing the threshold after each question to approximate a desired answering frequency, ensuring the system provides answers only when confidence scores exceed the threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static confidence threshold is used in a question answering system, then the system structure is simple and easy to implement, but the system cannot adapt to changing content and usage patterns, resulting in suboptimal performance
Solution Approach 1:
The patent applies dynamics by transforming the static confidence threshold into a dynamic one that automatically adjusts based on the distribution of confidence scores from multiple reasoning algorithms. The threshold is no longer fixed but evolves adaptively to match changing content and usage patterns, resolving the contradiction between adaptability and complexity.
Solution Approach 2:
The patent changes the parameter of the confidence threshold from a fixed value to a dynamically adjusted value based on the statistical distribution of confidence scores. By monitoring and adjusting this parameter in response to changing conditions, the system achieves adaptability without requiring complex manual reconfiguration.
2Reliability
If a high confidence threshold is set to ensure answer accuracy, then fewer incorrect answers are provided, but correct answers with moderate confidence are discarded
Solution Approach 1:
The patent dynamically adjusts the confidence threshold parameter based on the distribution of scores from multiple reasoning algorithms. When algorithms show high agreement (narrow score distribution), the threshold is raised to ensure accuracy. When there is greater variation (wide score distribution), the threshold is lowered to capture more potential correct answers, thus balancing reliability and productivity.
Solution Approach 2:
The system dynamically adapts the confidence threshold based on real-time analysis of algorithm performance and score distributions. This dynamic adjustment allows the system to maintain high reliability when possible while maximizing the answer provision rate when appropriate, resolving the contradiction between accuracy and productivity.
3Productivity
If a low confidence threshold is set to maximize answer provision, then more questions are answered, but the rate of incorrect answers increases
Solution Approach 1:
The patent implements dynamic parameter adjustment where the confidence threshold is continuously optimized based on the performance characteristics of multiple reasoning algorithms. This ensures that the threshold is low enough to maintain high answer provision rates when appropriate, but high enough to filter out incorrect answers, thus balancing productivity and reliability.
4Adaptability or versatility
If multiple reasoning algorithms are used to improve answer quality, then the system can better handle diverse questions, but the complexity of determining the confidence threshold increases
Solution Approach 1:
The patent applies self-service by enabling the system to automatically determine and adjust its own confidence threshold without external intervention. The system analyzes the confidence score distributions from multiple reasoning algorithms and autonomously optimizes the threshold, reducing the difficulty of threshold determination while maintaining the ability to handle diverse questions.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors the performance of multiple reasoning algorithms and uses this information to adjust the confidence threshold. This feedback loop simplifies threshold determination by using empirical data from algorithm performance rather than requiring manual calibration, thus resolving the contradiction between versatility and measurement difficulty.
Data Source
AI summary
A method includes receiving, by a question answering system having a confidence threshold, plural questions from one or more user devices. The method includes processing each one of the questions by: generating an answer to the one of the questions; determining a confidence score of the answer; in response to determining the confidence score is greater than the confidence threshold, increasing the confidence threshold and returning the answer to the user device that generated the one of the questions; and in response to determining the confidence score is less than the confidence threshold, decreasing the confidence threshold and not returning the answer to the user device that generated the one of the questions. The increasing the confidence threshold and the decreasing the confidence threshold are performed such that the question answering system returns answers for the plural questions at a frequency that approximates a pre-defined target answering frequency.