Context-Based Prompting for Accurate Natural-to-Query Translation
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Solution Overview
Problem
Current large language models (LLMs) struggle to accurately translate natural language queries into database query languages due to architectural variations and complexity, requiring costly and time-consuming manual configuration to provide database-specific context.
Innovation Solution
A method using a pool of 'shots' that include natural language queries and their corresponding database translations, vectorized into a common space, automatically selecting relevant shots for LLM prompts to enhance translation accuracy with minimal administrator overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual configuration is used to provide database-specific context for LLM translation, then translation accuracy is improved, but configuration time and cost increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically generating a comprehensive pool of shot examples that include natural language queries and their corresponding query language translations specific to the target database architecture. This pre-generated pool eliminates the need for time-consuming manual configuration while providing the necessary database-specific context for accurate translations.
Solution Approach 2:
The system enables self-service by allowing the LLM to automatically select and utilize relevant shot examples from the pre-generated pool based on the input natural language query. The system serves itself by autonomously identifying appropriate translation patterns without requiring manual administrator intervention, thus reducing configuration time while maintaining translation accuracy.
2Reliability
If comprehensive database context is provided to LLM for translation, then translation reliability is improved, but system complexity increases
Solution Approach 1:
The system segments the comprehensive database context into discrete, manageable shot examples that can be independently stored and retrieved. Each shot contains a natural language query and its corresponding translation, allowing the LLM to access only the specific segments relevant to the current translation task rather than processing the entire database context, thus reducing system complexity while maintaining reliability.
Solution Approach 2:
The system introduces an intermediary mechanism in the form of a shot pool that mediates between the LLM and the comprehensive database context. The shot pool acts as a buffer that pre-processes and organizes translation patterns, allowing the LLM to interact with simplified, pre-structured examples rather than raw database schema information, thereby reducing system complexity while preserving translation reliability.
3Measurement precision
If more shot examples are included in LLM prompt, then translation accuracy is improved, but processing time increases
Solution Approach 1:
The system applies partial action by including only a subset of the most relevant shot examples in each LLM prompt rather than the entire pool. The LLM selectively processes a manageable number of shots that are most applicable to the current query, achieving high translation accuracy without the processing overhead of examining all available examples, thus balancing accuracy with processing speed.
Solution Approach 2:
The system implements dynamics by adaptively adjusting the number and selection of shot examples included in each prompt based on the specific characteristics of the input query. The system dynamically determines the optimal subset of shots to include, allowing the translation process to be neither too rigid (always using fixed number of shots) nor too variable (potentially using all shots), thereby optimizing the balance between translation accuracy and processing efficiency.
Data Source
AI summary
A disclosed method facilitates translation of natural language queries into query language statements usable to retrieve data from or write data to a particular database. The method includes obtaining a pool of shots. Each shot in the pool includes a natural language query component and a corresponding database translation component. The method further provides for vectorizing the natural language query component for each of the shots into a common vector space; receiving a natural language query from a user interface; vectorizing the natural language query within the common vector space; identifying a subset of vectorized natural language query components that satisfy a similarity metric when compared to the vectorized natural language query; and generating an LLM prompt that includes shots from the pool corresponding to the subset of the vectorized natural language query.