Secondary Question Generation for Answer Confidence
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Solution Overview
Problem
Question-answering systems often produce incorrect answers due to missing information and fail to understand natural language questions effectively, leading to low confidence scores and inefficient information retrieval.
Innovation Solution
The system generates secondary questions to identify and acquire missing information by ranking hypotheses based on utility, using external sources to improve confidence scores and validate responses through crowd sourcing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the QA system uses a pre-structured database or collection of documents to answer questions, then the system can provide automated answers, but the system produces incorrect answers due to missing information and low confidence scores
Solution Approach 1:
The system performs preliminary analysis to identify missing information before generating final answers. By proactively detecting gaps in the available information and formulating secondary questions to acquire additional data, the system prevents incorrect answers rather than correcting them afterward, thereby improving answer accuracy while addressing information gaps
Solution Approach 2:
The system implements a feedback mechanism where confidence scores are continuously evaluated and used to trigger information acquisition. When confidence scores fall below thresholds indicating potential incorrectness, the system automatically formulates and submits secondary questions to external sources, creating a closed-loop feedback system that improves reliability by addressing missing information
2Measurement precision
If the QA system generates multiple candidate answers with confidence scores, then the system can evaluate answer quality, but the system requires additional processing to identify missing information and generate secondary questions
Solution Approach 1:
The system segments the question-answering process into distinct functional modules: candidate answer generation, confidence score evaluation, missing information identification, secondary question formulation, and information acquisition. This modular segmentation allows each component to be optimized independently while maintaining overall system precision in measuring answer confidence
Solution Approach 2:
The system introduces an intermediary component that acts as a bridge between confidence score evaluation and information acquisition. This intermediary analyzes confidence scores, identifies patterns indicating missing information, and selectively triggers secondary question generation only when necessary, thereby managing system complexity while maintaining measurement precision
3Reliability
If the system submits secondary questions to external sources to acquire missing information, then the system can improve confidence scores, but the system increases information retrieval time and processing overhead
Solution Approach 1:
The system applies partial action by submitting secondary questions selectively rather than for all candidate answers. Confidence scores are evaluated against thresholds, and secondary information acquisition is triggered only when scores indicate potential incorrectness, thereby improving answer confidence while minimizing unnecessary information retrieval time
Solution Approach 2:
The system dynamically adjusts parameters such as confidence score thresholds and secondary question submission criteria based on contextual factors. By changing these parameters adaptively, the system optimizes the balance between improving answer confidence through information acquisition and minimizing time loss from excessive information retrieval
Data Source
AI summary
A method of generating secondary questions in a question-answer system. Missing information is identified from a corpus of data using a computerized device. The missing information comprises any information that improves confidence scores for candidate answers to a question. The computerized device automatically generates a plurality of hypotheses concerning the missing information. The computerized device automatically generates at least one secondary question based on each of the plurality of hypotheses. The hypotheses are ranked based on relative utility to determine an order in which the computerized device outputs the at least one secondary question to external sources to obtain responses.