Natural Language Query Mapping With Schema-Guided LLM Prompts
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Solution Overview
Problem
Existing natural language processing systems struggle with accurately translating human queries into structured database queries due to a lack of context and reliance on statistical patterns, leading to ambiguity in interpreting queries like 'what is jay?', which could refer to the letter 'J' or the name 'Jay' or a bird species.
Innovation Solution
A system utilizing a large language model (LLM) processes natural language queries by generating prompts based on data schemas and predefined query-answer pairs to select the most appropriate database query, reducing the need for fine-tuning the LLM on specific data schemas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If natural language processing techniques are used to translate user queries into computer language, then human friendliness is improved, but accuracy and context understanding deteriorate due to lack of context and reliance on statistics
Solution Approach 1:
The patent introduces an intermediary system that bridges natural language and structured query languages. This intermediary uses data schemas and query templates to mediate between the user's natural language intent and the precise database query structure, resolving the contradiction by adding a translation layer that preserves both human friendliness and query accuracy
Solution Approach 2:
The system performs preliminary actions by pre-defining data schemas, query templates, and answer formats before processing user queries. This preliminary structuring enables the system to quickly match natural language queries to appropriate templates, maintaining accuracy without requiring complex real-time analysis
2Manufacturing precision
If data schemas and query templates are pre-defined to improve translation accuracy, then query execution precision is improved, but system complexity increases due to schema definition and template management
Solution Approach 1:
The patent creates universal query templates and data schemas that can handle multiple types of queries across different domains. These multi-functional templates reduce the need for numerous specialized schemas, thereby maintaining high translation accuracy while limiting the growth of system complexity
Solution Approach 2:
The system manages complexity by allowing parameter changes in existing templates rather than creating new schemas for each query type. By modifying parameters within predefined templates, the system adapts to different query requirements without increasing structural complexity
3Loss of time
If the system uses predefined query templates to reduce training needs, then model training time is reduced, but adaptability to new query types may worsen
Solution Approach 1:
The patent implements dynamic query template selection where the system adapts templates based on the incoming query's characteristics. This dynamic approach allows the system to handle new query types by selecting and adapting existing templates rather than requiring retraining, thus maintaining both speed and flexibility
Solution Approach 2:
The system segments queries into different categories and matches them with appropriate template types. This segmentation allows the system to handle diverse query types through specialized templates while maintaining overall system efficiency and avoiding the need for comprehensive retraining
Data Source
AI summary
A system and method for generating a database query based on a natural language query improves database utilization is presented. The method includes receiving a natural language query directed to a security database, wherein the security database includes a representation of a computing environment; selecting a first database query from a plurality of database queries; generating a second database query based on the first database query adapted by the received natural language query; and executing the second database query on the security database.