Method of classifying a very large corpus of documents

A client device for sorting a plurality of candidate documents into several sets each associated with a reference document, comprising a memory storing each candidate and reference document, wherein the method comprises a processor configured to implement:

• (a) for each reference document
• generating a first prompt for a first large language model requesting the generation of at least one question aiming to determine whether a candidate document is relevant to the content of the reference document, the first prompt containing a text of the reference document;
• transmitting each generated first prompt to a first server implementing the first large language model;
• receiving in reply to the first prompt at least one reference question corresponding to the reference document;
• (b) for each candidate document,
• generating at least one second prompt for a second large language model requesting the answer to at least one of the reference questions for the candidate document, the second prompt containing the reference question and a text of the candidate document;
• transmitting each generated second prompt to a second server implementing the second large language model,
• receiving in reply to each second prompt a value representative of the answer to the reference question;
• (c) assigning each candidate document to the set associated with a reference document as a function of the values(s) that has been received for a second prompt containing a reference question associated to the reference document.

A non-transitory computer-readable medium comprising program code instructions stored thereon for implementing a method for sorting a plurality of candidate documents into several sets each associated with a reference document, each candidate and reference document being stored by a memory of a client device, wherein the method comprises performing, by a processor of the device, steps of:

• (a) for each reference document
• generated a first prompt for a first large language model requesting the generation of at least one question aiming to determine whether a candidate document is relevant to the content of the reference document, the first prompt containing a text of the reference document;
• transmitting each generated first prompt to a first server implementing the first large language model;
• receiving in reply to the first prompt at least one reference question corresponding to the reference document;
• (b) for each candidate document,
• generating at least one second prompt for a second large language model requesting the answer to at least one of the reference questions for the candidate document, the second prompt containing the reference question and a text of the candidate document;
• transmitting each generated second prompt to a second server implementing the second large language model,
• receiving in reply to each second prompt a value representative of the answer to the reference question;
• assigning each candidate document to the set associated with a reference document as a function of the values(s) that has been received for a second prompt containing a reference question associated to the reference document.