Systems and methods for optimizing language models based on user context

Abstract

The subject technology uses context models to improve the performance of language models. The technology may introduce one or more context insights into the prompts for language models to personalize the outputs of the language models for particular users. The context insights may include consumer dimensions determined based on identity data for a user. The consumer dimensions may be generated using machine learning techniques that are applied to consumer data, event data, and transaction data. A unique context model may be trained for each consumer dimension to increase the accuracy and specificity of the context models. The personalized content generated by the language models may be included in a piece of content published on a publication network. The performance the language models may be optimized based on the performance of the published content.