Language Model Embedding Mapping for Disparate Language Datasets
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Solution Overview
Problem
Mapping disparate language-based datasets, such as natural language strings with weak logical connections or evolving relationships, is difficult due to the lack of a clear one-to-one correspondence and the dynamic nature of features and codes, making automated data mapping impractical.
Innovation Solution
A method involving a language model that generates prompt embedding vectors and term embedding vectors, combining them to create a mapping between features and terms, using a vector generation controller and a language model to process natural language data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated rule-based mapping is used for disparate language-based datasets, then mapping speed is improved, but mapping accuracy deteriorates due to weak logical connections and evolving relationships
Solution Approach 1:
The patent replaces traditional mechanical rule-based mapping systems with a language model-based semantic mapping system. The language model processes natural language descriptions of features and codes, understanding semantic relationships rather than relying on pre-defined rules. This substitution enables accurate mapping of disparate datasets with weak logical connections while maintaining automation.
Solution Approach 2:
The patent changes the fundamental parameter of mapping from rule-based syntactic matching to semantic-based vector embeddings. By transforming text descriptions into vector representations and using cosine similarity to measure relationships, the system can handle evolving relationships and weak logical connections that rule-based systems cannot accommodate.
2Device complexity
If one-to-one correspondence is enforced between features and codes, then mapping simplicity is improved, but mapping applicability deteriorates due to many-to-one relationships and evolving connections
Solution Approach 1:
The patent implements a universal mapping approach where a single language model can handle multiple mapping scenarios including one-to-one, one-to-many, and many-to-one relationships. The model processes natural language descriptions and generates appropriate mappings based on semantic understanding, making the system adaptable to various relationship types without requiring separate handling mechanisms.
Solution Approach 2:
The patent introduces dynamic mapping capabilities where the system can adapt to evolving relationships between features and codes over time. The language model processes updated natural language descriptions and relearns relationships as datasets evolve, maintaining accuracy without requiring manual reconfiguration of mapping rules.
3Measurement precision
If manual mapping rules are created for disparate datasets, then mapping precision is improved, but device complexity increases due to the need for custom rules for each dataset pair
Solution Approach 1:
The patent implements self-service mapping where the language model automatically learns and applies mapping rules without requiring manual creation of custom rules for each dataset pair. The model processes natural language descriptions of features and codes, automatically inferring relationships and generating mappings, thereby eliminating the complex task of manually creating and maintaining dataset-specific rules.
Data Source
AI summary
A method including receiving a prompt for a language model trained to process natural language data. The prompt commands the language model. The method also includes generating an updated prompt by at least injecting a feature into the prompt. The method also includes applying a vector generation controller to the updated prompt to generate a set of prompt embedding vectors. The method also includes applying the vector generation controller to a language dataset to generate a set of term embedding vectors. The language dataset includes terms disparate from the feature. The method also includes combining the set of term embedding vectors with the set of prompt embedding vectors to generate a set of combined vectors. The method also includes applying the language model to the set of combined vectors to generate a mapping between the feature and a term in the terms. The method also includes presenting the mapping.