Generative Tax Description System Using Vector Embeddings
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Solution Overview
Problem
Generating verbose tax category descriptions is a manual, time-consuming process that requires subject matter expertise, especially for complex tax categories, and is burdensome for companies operating on a national or global scale.
Innovation Solution
A computer system utilizing a generative language model (GLM) to automatically generate verbose tax category descriptions by extracting source text data, generating embeddings, querying a vector database, and inputting prompts to the GLM to produce accurate and informative descriptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual generation of verbose tax category descriptions is used, then accuracy and contextual understanding are improved, but time consumption and labor burden increase significantly
Solution Approach 1:
The patent introduces an embedding model as an intermediary between the tax category data and the generative language model. Source text data is converted to embeddings, which are stored in a vector database and used to generate context-aware prompts for the GLM. This intermediary process enables automated generation while maintaining accuracy by preserving semantic relationships in the embedded representations.
2Measurement precision
If manual generation by subject matter experts is used, then quality and accuracy of descriptions are improved, but scalability and productivity deteriorate
Solution Approach 1:
The system enables self-service automated generation of verbose tax category descriptions through a multi-component architecture. The embedding model automatically converts source text to embeddings, the vector database retrieves relevant context, and the generative language model produces descriptions without requiring manual intervention from subject matter experts, thereby dramatically increasing productivity while maintaining quality.
3Loss of information
If comprehensive contextual information is included in descriptions, then usefulness and informativeness are improved, but complexity of generation process increases
Solution Approach 1:
The patent segments the description generation process into distinct functional components: an embedding model that converts source text to vector representations, a vector database that stores and retrieves relevant embeddings, and a generative language model that produces descriptions. This segmentation allows each component to specialize in one task, managing overall system complexity while enabling comprehensive contextual information to be incorporated through the embedding-retrieval-generation pipeline.
Data Source
AI summary
A computing system for generating verbose tax category descriptions includes a computing device with processing circuitry configured to identify defined tax categories. For each defined tax category, the processing circuitry is configured to extract source text data associated with the defined tax category, generate embeddings representing the source text data, and store the source text embeddings in a vector database. The processing circuitry is further configured to receive an instruction requesting a verbose tax category description of an indicated tax category, generate instruction text embeddings, and query the vector database with the instruction text embeddings to identify a subset of matching source text embeddings. The processing circuitry is further configured to retrieve matching source text data, generate a prompt for a generative language model, input the prompt into the model to generate verbose tax category description text, and output the verbose tax category description text.