Abstract: Embodiments of the present disclosure generally relate to methods for autonomous orchestration of a data labeling workflow. Embodiments include generating an embedding of an input string related to input data. Embodiments include selecting a set of candidate labels from a plurality of labels based on the embedding. Embodiments include selecting a refined set of candidate labels based on using a cross-encoder model to generate a respective joint embedding of the input string and each respective label in the set of candidate labels. Embodiments include providing natural language descriptions of the refined set of candidate labels and the input string to a language processing machine learning model. Embodiments include assigning a label to the input data based on the language processing machine learning model outputting the label in response to the natural language descriptions of the refined set of candidate labels and the input string.

Abstract: Embodiments of the present disclosure generally relate to methods for autonomous orchestration of a data labeling workflow. Embodiments include generating, using a generative machine learning model, a natural language description of input data. Embodiments include identifying candidate labels that are semantically similar to the natural language description of the input data. Embodiments include providing natural language descriptions of the candidate labels and the natural language description of the input data to a language processing machine learning model. Embodiments include receiving an output from the language processing machine learning model in response to the natural language descriptions of the candidate labels and the natural language description of the input data, wherein the output indicates a selected label from the candidate labels. Embodiments include validating the output based on an alternative label determination technique.