Interactive Assistant Reliability Metrics for Stage-Level Misbehavior
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Solution Overview
Problem
Existing metrics for evaluating interactive software applications, such as conversational chatbots, often overestimate reliability and fail to identify surface misbehaviors, particularly due to variations in components and constant updates across different versions.
Innovation Solution
Implementing metrics that evaluate and track surface misbehavior of interactive software applications by categorizing user queries into predefined stages and applying labels to identify and alert on unsatisfactory performance, allowing for continuous monitoring and improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing metrics are used to evaluate interactive software applications, then evaluation can be performed, but reliability is overestimated and surface misbehaviors are not identified
Solution Approach 1:
The evaluation process is segmented into distinct stages (invocation stage, input-receiving stage, response-generation stage, response-rendering stage), with each stage having specific labels and evaluation criteria. This segmentation enables precise identification of misbehaviors at different points in the interaction flow, resolving the contradiction by providing both reliable overall evaluation and precise misbehavior detection.
Solution Approach 2:
Different labels are assigned to different stages based on their specific characteristics and failure modes. Each stage has customized evaluation metrics (e.g., invocation time for invocation stage, response accuracy for response-generation stage), allowing precise measurement of misbehaviors specific to each stage while maintaining reliable overall reliability assessment.
2Adaptability or versatility
If components are constantly updated to cope with emerging functionality, then adaptability improves, but metric applicability across versions becomes difficult
Solution Approach 1:
The evaluation framework uses universal labels and stages that apply across different versions and types of interactive software applications. The same four stages and their corresponding labels can be used to evaluate NLU-based assistants, LLM-based assistants, and other variants, enabling consistent metric application despite component updates and functional changes.
Solution Approach 2:
The framework dynamically adapts to different application versions by allowing flexible configuration of stage-specific parameters and labels while maintaining the overall stage structure. This enables the same evaluation framework to accommodate evolving components and functionality without requiring metric redesign.
3Quantity of substance
If user queries are included in evaluation without filtering, then evaluation coverage increases, but subjective user behaviors contaminate objective reliability measurement
Solution Approach 1:
User queries are pre-filtered and classified into eligible and ineligible categories before evaluation. This preliminary action ensures that only objectively measurable queries are included in reliability calculations, while maintaining broad coverage by still evaluating the full range of user interactions and using the filtered set for precise reliability measurement.
Data Source
AI summary
Implementations relate to retrieving and processing metadata associated with a user query directed to an interactive assistant application. Implementations further relate to classifying the user query using labels assigned to invocation stage, input-receiving stage, response-receiving stage, and/or response-rendering stage of the user query that are determined based on processing the metadata associated with the user query. Whether the user query can be applied to evaluate a performance (e.g., surface reliability) of the interactive assistant application can be determined based on the classification of the user query.