Query Conversion Model Training With Ranked Example Selection
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Solution Overview
Problem
Language models often provide inaccurate or nonsensical responses to open-ended queries due to their inability to interpret general or ambiguous questions, leading to user frustration.
Innovation Solution
A ranking model is trained using a pairwise ranking loss function to select a subset of examples from a large dataset, enhancing the conversion model's performance by maximizing the probability of accurate conversions between natural language queries and domain-specific instance data structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a language model is trained to answer open-ended queries, then the model can provide responses to general questions, but the responses become inaccurate or hallucinatory
Solution Approach 1:
The patent introduces a conversion model as an intermediary component that translates open-ended natural language queries into structured domain-specific queries before they reach the database. This mediator ensures that only properly formatted, accurate queries are executed, preventing hallucinations while maintaining the ability to handle general user questions through intelligent query transformation.
Solution Approach 2:
The system divides the query processing into separate functional stages: a conversion model that handles natural language to structured query transformation, and a separate database execution layer. This segmentation allows each component to specialize - the conversion model focuses on understanding general queries while the database layer ensures accurate, structured data retrieval, thereby resolving the accuracy-versus-versatility contradiction.
2Ease of operation
If a language model processes queries without structured formatting, then user interaction remains natural and simple, but the model cannot accurately interpret or retrieve information from the database
Solution Approach 1:
The conversion model serves as a bridge between simple natural language input and precise structured query requirements. It maintains ease of operation by accepting colloquial user questions while simultaneously transforming them into accurately formatted database queries, thus preserving both simplicity and precision without compromise.
Solution Approach 2:
The system changes the parameter format of queries from unstructured natural language to structured domain-specific syntax. This parameter transformation enables the database to retrieve information with high precision while the conversion model ensures the original natural language intent is preserved, resolving the contradiction between ease of input and retrieval accuracy.
3Manufacturing precision
If the conversion model uses a large training dataset for query conversion, then the model achieves high conversion accuracy, but the training process becomes computationally expensive and time-consuming
Solution Approach 1:
The patent employs partial action by training the conversion model on a curated subset of high-quality example pairs rather than exhaustively processing the entire database. This selective training approach achieves sufficient conversion accuracy for practical purposes while significantly reducing training time and computational resources compared to full-dataset training.
Solution Approach 2:
The system performs preliminary training on representative example data that captures the essential query conversion patterns. By preparing and storing these pre-learned conversion rules and examples during training, the model can quickly and accurately convert new queries without requiring extensive real-time computation, thus reducing operational time while maintaining high accuracy.
Data Source
AI summary
A method including receiving a training dataset including instance data structures storing natural language queries in a domain-specific language. The domain-specific language includes predefined terms representable in a predefined data structure. The natural language queries corresponds to the instance data structures. The method also includes training, until convergence, a ranking model on the training dataset using a pairwise ranking loss function that modifies a sequence of layers of the ranking model to maximize a score output by the ranking model. The score represents a probability that a combination of an instance data structure and a corresponding natural language query, when inserted into a conversion prompt of a conversion model, increases a performance of the conversion model when converting a new natural language query into a new instance data structure. After convergence, the ranking model is returned as a trained ranking model.