Computer-implemented methods for enhancing temporal understanding in retrieval-augmented generation through temporal-aware retrieval

Abstract

The present disclosure relates to methods, apparatus, systems, and non-transitory computer-readable storage media for performing a temporal-aware retrieval-augmented generation (RAG) for machine learning. According to some examples, a computer-implemented method includes receiving, by a provider network, a search query from a user device; performing, by the provider network, a search based on the search query to generate a set of candidate documents; generating, by one or more machine learning models of the provider network, a semantic rank of the set of candidate documents for the search query; determining, by the one or more machine learning models of the provider network, a temporal expression in the search query; generating, by the one or more machine learning models of the provider network, a temporal rank of the set of candidate documents for the search query based at least in part on the temporal expression; generating, by the one or more machine learning models of the provider network, a combined rank of the set of candidate documents based on the semantic rank and the temporal rank; generating, by the one or more machine learning models of the provider network, a response based on the search query and a retrieval-augmented generation (RAG) input of the combined rank; and sending, by the provider network, the response to the user device.