Large-Model Query Generation With Field-Value Semantic Matching
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Solution Overview
Problem
Existing data query methods based on natural language face challenges in accurately handling synonyms, abbreviations, and spelling errors, leading to inaccurate retrieval of relevant information and a negative impact on user experience and query accuracy.
Innovation Solution
A method involving matching a query question with a target field value group to reduce semantic deviation, using a large model to generate a target query statement based on prompt information derived from the query question, target field values, and data table descriptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If natural language query methods are used to lower the threshold for data query, then ease of operation is improved, but query accuracy deteriorates due to semantic deviation in understanding synonyms, abbreviations, and spelling errors
Solution Approach 1:
The patent introduces an intermediary processing layer between natural language input and structured query execution. This layer includes semantic understanding modules that act as mediators to bridge the gap between user-friendly natural language and precise database queries, resolving the contradiction by adding translation and disambiguation steps without increasing user complexity
Solution Approach 2:
The system dynamically adjusts processing parameters based on query characteristics. When semantic deviation is detected (such as synonyms or abbreviations), the system changes its understanding parameters to apply appropriate disambiguation strategies, thereby maintaining high query accuracy while preserving natural language input simplicity
2Measurement precision
If semantic understanding processing is enhanced to handle synonyms and abbreviations, then query accuracy is improved, but system complexity increases
Solution Approach 1:
The semantic understanding system is divided into modular components: synonym recognition modules, abbreviation expansion modules, spelling correction modules, and disambiguation modules. Each segment handles specific aspects of semantic deviation independently, improving query accuracy through specialized processing while keeping overall system complexity manageable through modular architecture
Solution Approach 2:
The patent implements universal semantic understanding components that can handle multiple types of deviations (synonyms, abbreviations, spelling errors) through a unified framework. This multi-functional approach improves query accuracy across different error types without proportionally increasing system complexity, as the same core modules serve multiple purposes
3Measurement precision
If field value matching is performed to reduce semantic deviation, then retrieval accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary field value matching and semantic normalization before the main query execution. By pre-processing and pre-matching field values, the system reduces semantic deviation early in the pipeline, improving retrieval accuracy while minimizing the time impact on the critical query execution path through efficient preprocessing strategies
Data Source
AI summary
A method for information display based on a large model, a device, and a medium are provided. The method includes: in response to receiving a query question, performing matching between the query question and a field value set of a target data table corresponding to the query question to obtain at least one target field value group, where the target field value group includes target field values semantically associated with each other, and the target field values are configured to reduce a semantic deviation in the large model's understanding of the query question; invoking the large model according to a prompt information to generate a target query statement, where the prompt information is obtained based on the query question, the at least one target field value group, and a description information of the target data table; and displaying a query result obtained by executing the target query statement.