Taxonomy extraction using an llm

A method for processing tax return data, comprising:

• receiving, by a processing server, tax return data in a structured format comprising a schema defining the tax return data;
• extracting, by the processing server, features from the tax return data according to the schema;
• classifying, by the processing server, the extracted features using a machine learning model based on historical tax return patterns;
• classifying, in parallel with the machine learning model, by a large language model (LLM), the extracted features based on contextual understanding;
• analyzing, by an engine, the classified data from the machine learning model and the LLM to generate a hierarchical taxonomy of the tax return data sorted into categories and subcategories, wherein the engine selects between outputs of the machine learning model and the LLM based on confidence scores and a data type of the extracted features, the engine weighting the machine learning model output for structured numerical data and weighting the LLM output for unstructured textual data;
• analyzing, by the LLM, the hierarchical taxonomy of the tax return data;
• displaying, by a display device, the generated taxonomy to a user through an interactive interface;
• and
• training, based on the confidence scores, one or more of the machine learning model and the LLM via a feedback loop.