Machine learning model dynamic token screening and enhanced response generation

Abstract

Aspects of the present disclosure relate to dynamic token screening and enhanced response generation using machine learning models. Embodiments include determining, by a machine learning model, a baseline attention weight for each token of a plurality of tokens contained in a text string received as an input to the machine learning model. Embodiments include identifying one or more protected tokens of the plurality of tokens contained in the text string and generating a dropout probability for each protected token of the one or more protected tokens. Embodiments include determining, by the machine learning model, a revised attention weight for each token of the plurality of tokens based on the dropout probability for each protected token. Embodiments include generating, by the machine learning model, an output based on the text string and the revised attention weight for each token. Embodiments include providing the output in response to the input.