Query Intent Model Validation via Confidence Threshold Calibration
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Solution Overview
Problem
In multi-tenant database systems, accurately determining query categorization and confidence for search queries is challenging due to varying data across tenants, making it difficult to manually verify and calibrate models, which affects search result precision and relevance.
Innovation Solution
A system that automatically determines and sets confidence thresholds for query categorization using synthetic queries and structured documents, allowing for precise filtering and ranking of search results based on confidence values, with two thresholds to balance false positives and negatives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a model is trained for query categorization using large amounts of data, then the model accuracy is improved, but the device complexity and difficulty of manual verification increase
Solution Approach 1:
The system performs self-validation by automatically executing queries against the model and comparing results without requiring manual verification. The validation process is autonomous, where the system uses its own resources (queries, results, and processing logic) to assess model performance for each tenant independently.
Solution Approach 2:
The validation process is segmented into tenant-specific executions, where each tenant's data and queries are processed independently. The system divides the validation task into discrete steps: executing queries, retrieving results, comparing against expected outcomes, and calculating accuracy metrics for each tenant separately.
2Measurement precision
If custom models are created for each tenant based on specific data sets, then the model accuracy for each tenant is improved, but the device complexity and time required increase
Solution Approach 1:
A single universal validation system serves all tenants by automatically adapting to each tenant's specific data and queries. The system uses a common validation framework that can process any tenant's data independently, eliminating the need for separate manual calibration processes for each tenant.
Solution Approach 2:
The system performs preliminary validation by automatically executing queries and comparing results before full deployment. This preliminary automated validation calibrates the model for each tenant in advance, ensuring accuracy is established before the system is fully operational for that tenant.
3Measurement precision
If manual verification of model accuracy is performed for each tenant, then the measurement precision is improved, but the ease of operation and productivity decrease
Solution Approach 1:
The validation system is self-executing and does not require system administrator intervention. It automatically retrieves tenant queries, executes them against the model, compares results, and generates accuracy reports independently, freeing administrators from manual verification tasks.
Solution Approach 2:
The system provides automated feedback by comparing actual query results against expected outcomes and calculating accuracy metrics. This feedback loop operates automatically for each tenant, providing precise accuracy measurements without requiring manual inspection or administrator involvement.
Data Source
AI summary
A system for improving a query response includes an interface and a processor. The interface is configured to receive a query. The processor is configured to determine a categorization for the query using a model. The categorization is associated with a confidence value. The processor is configured to a comparison of the confidence value and a first threshold, wherein the first threshold is determined to maximize a metric and modify the query response based at least in part on the categorization and the comparison.