Cognitive Visual Debugger for Question Answering Error Analysis
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Solution Overview
Problem
Current question answering systems require extensive manual effort to identify and correct errors, as users lack insight into why a system provides incorrect answers, leading to inefficiencies in improving accuracy.
Innovation Solution
A cognitive visual debugger is implemented, which instantiates multiple instances of a question answering system with specific modifications to each pipeline stage, allowing for parallel evaluation of changes to identify improvements in answer accuracy and presenting visual feedback on changes made to enhance question answering results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual error analysis is performed in question answering systems, then users can identify incorrect answers, but extensive manual effort is required and users lack insight into why errors occur
Solution Approach 1:
The system automatically performs error analysis by instantiating multiple modified QA system instances and analyzing their results, enabling the system to self-diagnose errors without requiring extensive manual user effort. The automated analysis includes generating visual debuggers and machine learning summaries to identify and correct errors autonomously.
Solution Approach 2:
The patent introduces an intermediary error analysis component that acts as a mediator between the QA system and users. This component instantiates multiple modified system instances, compares their results, and generates visual debuggers that explain errors, thereby providing users with insight into why errors occur without requiring direct manual analysis.
2Reliability
If multiple instances of QA system are instantiated with modifications for error analysis, then improved error detection is achieved, but system complexity increases
Solution Approach 1:
The patent segments the QA system into multiple independent instances, each with specific modifications to different pipeline stages. This segmentation allows parallel evaluation of changes while maintaining manageable complexity through modular architecture, where each instance can be independently configured and analyzed.
Solution Approach 2:
The patent adds a new dimension to system evaluation by creating multiple instances with varying modifications rather than analyzing a single system. This dimensional approach to error analysis enables comprehensive reliability improvement while organizing complexity systematically through structured instance management and comparison.
3Productivity
If automated error analysis is implemented, then manual effort is reduced, but computational resources and processing time increase
Solution Approach 1:
The patent applies partial action by instantiating multiple modified QA system instances selectively focused on specific pipeline stages rather than replicating the entire system exhaustively. This approach achieves effective error analysis with reduced computational overhead compared to complete system replication, balancing productivity improvement with resource conservation.
Data Source
AI summary
A mechanism is provided in a data processing system for conducting error analysis for a question answering system. Responsive to the question answering system generating one or more candidate answers for an input question, wherein the one or more candidate answers are determined to be incorrect, the mechanism instantiates a plurality of instances of the question answering system with a modification to each instance. The mechanism provides the input question to each of the plurality of instances of the question answering system. The mechanism analyzes results from the plurality of instances of the question answering system to identify at least one modification that led to improved results. The mechanism presents a graphical output based on the analysis.