Chatbot Response Weighting Using Query-Aware Neural Evaluation
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Solution Overview
Problem
Existing chatbot evaluation methods rely on static weights for assessing answer properties, which limits the adaptability and accuracy of chatbot performance evaluation.
Innovation Solution
Utilizing neural networks to dynamically adjust weights based on query characterization, including topic and user sentiment analysis, to generate sub-scores for chatbot responses, and aggregate these into a comprehensive evaluation score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static weights are assigned to different properties of chatbot answers, then the evaluation method is simple and easy to implement, but the adaptability and accuracy of chatbot performance evaluation deteriorates
Solution Approach 1:
The patent transforms the static weight assignment into a dynamic system where weights are adjusted based on query characteristics. A neural network model analyzes query properties (topic, complexity, user intent) and dynamically determines appropriate weights for different answer properties (accuracy, completeness, conciseness), allowing the evaluation system to adapt to different query types while maintaining implementation feasibility through automated processing.
Solution Approach 2:
The patent changes the parameters of the evaluation system by introducing query-characteristic-dependent weight adjustments. Instead of fixed weights, the system modifies weight parameters based on analyzed query properties, enabling the same evaluation framework to handle diverse query types with appropriate weighting schemes without requiring complete system redesign.
2Device complexity
If static weights are assigned to different properties of chatbot answers, then the evaluation system has low complexity, but the accuracy of chatbot performance evaluation deteriorates
Solution Approach 1:
The system introduces dynamic weight adjustment through neural network processing, where weights are continuously adapted based on query characteristics. This dynamic approach significantly improves evaluation accuracy by matching weight schemes to specific query types, while the modular architecture keeps system complexity manageable through standardized processing pipelines.
Solution Approach 2:
The patent introduces an intermediary neural network component that bridges the simple evaluation framework and accurate results. This intermediary analyzes query characteristics and translates them into appropriate weight configurations, enabling accurate evaluations without requiring complete system redesign or excessive complexity.
3Adaptability or versatility
If dynamic weight adjustment based on query characterization is implemented, then the adaptability and accuracy of chatbot evaluation is improved, but the computational complexity and processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network model on comprehensive query and answer data before deployment. This pre-training establishes robust query characterization capabilities and weight adjustment patterns, allowing the system to perform accurate dynamic evaluations during operation without requiring complex real-time computations, thus reducing operational computational complexity.
Solution Approach 2:
The system implements feedback mechanisms where evaluation results and query characteristics are continuously analyzed to refine weight adjustments. This feedback loop enables the system to learn from past evaluations and improve its weight assignment strategies, reducing the need for excessively complex computational rules while maintaining high adaptability.
4Measurement precision
If dynamic weight adjustment based on query characterization is implemented, then the accuracy of chatbot performance evaluation is improved, but the processing time increases
Solution Approach 1:
The neural network model undergoes extensive pre-training on diverse query and answer data before deployment, establishing optimized query characterization and weight adjustment patterns. This preliminary action enables the system to perform accurate evaluations during operation with minimal processing time, as the complex decision-making patterns are already established in the pre-trained model.
Solution Approach 2:
The patent replaces traditional mechanical or rule-based weight adjustment mechanisms with neural network-based processing. This substitution enables parallel processing of query characteristics and weight determination, significantly reducing processing time compared to sequential rule-based systems while maintaining or improving evaluation accuracy through the neural network's pattern recognition capabilities.
Data Source
AI summary
Apparatuses, systems, and techniques to cause weights assigned to properties of chatbot answers to be dynamically adjusted. In at least one embodiment, one or more neural networks are used to characterize one or more chatbot queries and cause weight values assigned to properties of answers to the one or more chatbot queries to be dynamically adjusted, based, at least in part, on the characterization of the one or more chatbot queries.