Dynamic Algorithm Selection for Question Answering Systems
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Solution Overview
Problem
Existing question answering systems face challenges in efficiently handling open domain questions due to the need for hundreds of algorithms, with varying computational difficulty and resource requirements, making it difficult to achieve performance goals while minimizing resource usage.
Innovation Solution
A method and system that utilize algorithm components based on categories, capturing performance and correctness metrics to determine efficient algorithm sets for specific question categories, employing a machine learning model to dynamically control algorithm execution and resource allocation for real-time question answering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hundreds of algorithms are used to handle open domain questions, then the system can achieve high accuracy in answering diverse questions, but the computational resource usage and system complexity increase significantly
Solution Approach 1:
The patent segments the question answering task by categorizing questions into different types (e.g., factual, analytical, creative) and selecting specific algorithms for each category. This segmentation allows the system to use only the necessary algorithms for each question type rather than deploying all hundreds of algorithms universally, thereby reducing system complexity while maintaining answer accuracy.
Solution Approach 2:
The patent applies local quality by tailoring the algorithm selection to the specific characteristics of each question category. Different question types receive different algorithmic treatments based on their unique requirements, ensuring high accuracy for each category while avoiding the overhead of using all algorithms for every question type.
2Adaptability or versatility
If hundreds of algorithms are deployed to parse and decompose questions, then the system can handle diverse open domain questions effectively, but the computational difficulty and resource requirements increase
Solution Approach 1:
The patent implements dynamic algorithm selection where the system adapts its algorithm choices based on the real-time characteristics of each incoming question. By dynamically categorizing questions and selecting appropriate algorithms, the system maintains high adaptability for diverse question types while optimizing computational resource usage by avoiding unnecessary algorithm executions.
Solution Approach 2:
The patent changes the parameter of algorithm selection from a static configuration to a dynamic decision based on question categorization. This parameter change allows the system to adjust its computational resource allocation according to the specific needs of each question, maintaining versatility while reducing overall computational burden.
3Reliability
If all algorithms are executed for every question, then the system ensures thorough analysis and high answer confidence, but the processing time and resource consumption increase
Solution Approach 1:
The patent applies partial action by executing only the subset of algorithms necessary for each question category rather than running all algorithms for every question. This approach maintains sufficient answer confidence by selecting algorithms specifically designed for each question type while significantly improving processing efficiency by avoiding redundant computations.
Data Source
AI summary
Mechanisms are provided in a data processing system for utilizing algorithms based on categories in a question answering system. The mechanisms capture a history of performance and correctness metrics for identifying efficiency of respective algorithms for finding answers to questions in respective question categories in a question answering system. The mechanisms determine sets of algorithms to use for respective question categories according to efficiency and correctness analysis. The mechanisms determine a question category of a given input question and execute a set of algorithms corresponding to the question category of the given input question that meet an efficiency threshold to contribute to finding a correct answer for the given input question.