System and method for compressing prompts to language models for document processing

A system comprising:

• a memory having computer-readable instructions stored thereon;
• and
• a processor that executes the computer-readable instructions to:
• receive a set of documents from which to generate a topic label and a topic description for a topic, wherein the topic label comprises a name for the topic and the topic description comprises a description of the topic in a human-understandable format;
• input the set of documents into an unsupervised machine learning model;
• execute the unsupervised machine learning model to output the topic for the set of the documents, the topic comprising a plurality of topic terms;
• select a subset of topic documents from the set of documents, wherein the subset of topic documents belong to the topic and are selected based on the plurality of topic terms;
• input the subset of topic documents into an information extraction model;
• execute the information extraction model to generate a plurality of snippets from the subset of topic documents for the topic;
• generate a compressed representation of the set of documents based on the plurality of snippets to include in a prompt, wherein the compressed representation having a first number of tokens to be stored in a computer memory that is less than a second number of tokens in the plurality of topic terms, the compressed representation being generated by concatenating the plurality of snippets generated from the selected subset of topic documents for the topic and excluding snippets from unselected topic documents for the topic;
• input the prompt of the topic into a language model;
• and
• generate the topic label and topic description for the topic by executing the language model based on the prompt.

A method comprising:

• receiving, by a processor executing computer-readable instructions stored on a memory, a set of documents from which to generate a topic label and a topic description for a topic, wherein the topic label comprises a name for the topic and the topic description comprises a description of the topic in a human-understandable format;
• inputting, by the processor, the set of documents into an unsupervised machine learning model;
• executing, by the processor, the unsupervised machine learning model to output the topic for the set of the documents, the topic comprising a plurality of topic terms;
• selecting, by the processor, a subset of topic documents from the set of documents, wherein the subset of topic documents belong to the topic and are selected based on the plurality of topic terms;
• inputting, by the processor, the subset of topic documents into an information extraction model;
• executing, by the processor, the information extraction model to generate a plurality of snippets from the subset of topic documents for the topic;
• generating, by the processor, a compressed representation of the set of documents based on the plurality of snippets to include in a prompt, wherein the compressed representation having a first number of tokens to be stored in a computer memory that is less than a second number of tokens in the plurality of topic terms, the compressed representation being generated by concatenating the plurality of snippets generated from the selected subset of topic documents for the topic and excluding snippets from unselected topic documents for the topic;
• inputting, by the processor, the prompt of the topic into a language model;
• and
• generating, by the processor, the topic label and topic description for the topic by executing the language model based on the prompt.