Abstract: A method includes applying a document ranking layer of a document selection large language model (LLM) to a document list including multiple reference documents to obtain a ranked document list. The method further includes selecting a subset of reference documents from the ranked document list and processing a user prompt and the document subset by a field LLM to generate an answer. The method further includes ranking the answer with an answer score by a ranking LLM. The method further includes ranking the document subset by the ranking LLM to obtain a ranked document subset. The method further includes calculating a loss function of a preference optimization layer of the document selection LLM based on the answer score and updating at least one training parameter of a foundation model of the document selection LLM based on the loss function of the preference optimization layer.

Abstract: A method includes applying a document ranking layer of a document selection large language model (LLM) to a document list including multiple reference documents to obtain a ranked document list. The method further includes selecting a subset of reference documents from the ranked document list and processing a user prompt and the document subset by a field LLM to generate an answer. The method further includes ranking the answer with an answer score by a ranking LLM. The method further includes ranking the document subset by the ranking LLM to obtain a ranked document subset. The method further includes calculating a loss function of a preference optimization layer of the document selection LLM based on the answer score and updating at least one training parameter of a foundation model of the document selection LLM based on the loss function of the preference optimization layer.