Unstructured document type classification with vector space similarity to llm generated documents

A system comprising:

• at least one processor;
• and
• a memory storing instructions that, when executed by the at least one processor, cause the system to perform operations comprising:
• based on a large language model, generating at least one document of a predetermined document type associated with sensitive data;
• generating embedding vectors associated with documents of the predetermined document type including the at least one document;
• based on the embedding vectors, determining a vector corresponding to the predetermined document type;
• classifying a second document that is unstructured as the predetermined document type based on satisfaction of a similarity threshold between an embedding vector associated with the second document and the vector corresponding to the predetermined document type;
• based on the large language model, generating a plurality of documents of a new document type in the absence of preexisting documents of the new document type, wherein
• the large language model is provided with a prompt configured to cause generation of the new document type,
• the prompt includes a field specifying the new document type, and
• the prompt includes an instruction to generate mock text files;
• generating embedding vectors associated with the plurality of documents of the new document type;
• and
• based on the embedding vectors associated with the plurality of documents of the new document type, determining a vector corresponding to the new document type.

A non-transitory computer-readable storage medium including instructions that, when executed by at least on processor of a computing system, cause the computing system to perform operations comprising:

• based on a large language model, generating at least one document of a predetermined document type associated with sensitive data;
• generating embedding vectors associated with documents of the predetermined document type including the at least one document;
• based on the embedding vectors, determining a vector corresponding to the predetermined document type;
• classifying a second document that is unstructured as the predetermined document type based on satisfaction of a similarity threshold between an embedding vector associated with the second document and the vector corresponding to the predetermined document type;
• based on the large language model, generating a plurality of documents of a new document type in the absence of preexisting documents of the new document type, wherein
• the large language model is provided with a prompt configured to cause generation of the new document type.
• the prompt includes a field specifying the new document type, and
• the prompt includes an instruction to generate mock text files;
• generating embedding vectors associated with the plurality of documents of the new document type;
• and
• based on the embedding vectors associated with the plurality of documents of the new document type, determining a vector corresponding to the new document type.