Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for training a multilingual large language model to embed text into an embedding space of a vision language model comprising a text encoder for a first language and a vision encoder. In particular, in some embodiments, the disclosed systems generate, utilizing the vision encoder, image embeddings for images. Additionally, in some embodiments, the disclosed systems generate, utilizing the multilingual large language model, text embeddings for text in languages other than the first language. Furthermore, in some embodiments, the disclosed systems determine similarity metrics between the image embeddings for the images and the text embeddings for the text. Moreover, in some embodiments, the disclosed systems adjust parameters of the multilingual large language model to reduce an output of a contrastive loss function based on the similarity metrics without adjusting parameters of the vision encoder.

Abstract: The present disclosure relates to systems for identifying instances of natural language input, determining intent classifications associated with instances of natural language input, and generating responses based on the determined intent classifications. In particular, the disclosed systems intelligently identify and group instances of natural language input based on characteristics of the user input. Additionally, the disclosed systems determine intent classifications for the instances of natural language input based message queuing in order to delay responses to the user input in ways that increase accuracy of the responses, while retaining a conversational aspect of the ongoing chat. Moreover, in one or more embodiments, the disclosed systems generate responses utilizing natural language.

Abstract: The present disclosure relates to systems for identifying instances of natural language input, determining intent classifications associated with instances of natural language input, and generating responses based on the determined intent classifications. In particular, the disclosed systems intelligently identify and group instances of natural language input based on characteristics of the user input. Additionally, the disclosed systems determine intent classifications for the instances of natural language input based message queuing in order to delay responses to the user input in ways that increase accuracy of the responses, while retaining a conversational aspect of the ongoing chat. Moreover, in one or more embodiments, the disclosed systems generate responses utilizing natural language.