Verbosity Adjustment in Automated Q&A via Reinforcement Learning
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Solution Overview
Problem
Current automated question and answer (Q and A) systems fail to optimize verbosity in their responses, leading to user confusion and increased follow-up responses, which in turn increases repair costs and data transmission.
Innovation Solution
A computer-implemented method that identifies choices with varying verbosity levels, normalizes multi-dimensional verbosity vectors, determines a state definition, and uses a trained reinforcement learning module to select the most suitable response, minimizing user confusion and optimizing conversation efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the Q and A system provides detailed responses to ensure clarity, then user understanding is improved, but conversation efficiency deteriorates and follow-up responses increase
Solution Approach 1:
The system dynamically adjusts the verbosity level of responses based on the conversation state and user needs. The reinforcement learning module learns to select appropriate verbosity levels (e.g., low, medium, high) for different conversation contexts, allowing the system to provide sufficient detail when needed while maintaining brevity when appropriate, thereby resolving the contradiction between ensuring understanding and maintaining efficiency
Solution Approach 2:
The system changes the verbosity parameter of responses based on learned patterns from conversation data. By normalizing multi-dimensional verbosity vectors and using reinforcement learning to determine optimal state definitions, the system can adjust response length and detail levels to match the specific conversation context, achieving both user understanding and conversation efficiency
2Ease of operation
If the Q and A system uses simple language to avoid confusion, then ease of operation is improved, but information completeness deteriorates
Solution Approach 1:
The system applies different language complexity levels to different parts of the response based on the conversation context. The reinforcement learning module determines which portions of information require simplification and which can maintain technical precision, allowing the system to preserve important information while using accessible language where appropriate
3Reliability
If the Q and A system provides comprehensive responses to prevent confusion, then user understanding is improved, but data transmission volume increases
Solution Approach 1:
The system provides partially detailed responses by selecting the appropriate verbosity level for each conversation turn. The reinforcement learning module determines the minimum necessary detail required to prevent confusion, avoiding excessive information transmission while maintaining sufficient clarity, thereby reducing data volume while preserving user understanding
Data Source
AI summary
In one general aspect, a computer-implemented method includes identifying current choices with different verbosity levels for a current turn in a conversation; normalizing multi-dimensional verbosity vectors for each of the current choices to obtain a normalized value for each of the current choices; determining a state definition for the current turn in the conversation, utilizing the normalized values for each of the current choices; providing the state definition for the current turn in the conversation and the normalized values for each of the current choices to a trained reinforcement learning module; receiving, from the trained reinforcement learning module, a score associated with each of the current choices for the current turn in the conversation; and selecting one of the current choices to be entered for the current turn in the conversation, based on the score associated with each of the current choices for the current turn in the conversation.