Collaborative development of machine learning models on specific concepts

Abstract

A system and method and for collaboratively developing one or more concepts in a machine-learning (ML) model includes receiving a set of user generated data points and training a local ML model based on the user generated data points. A first prompt is generated based on the set of user generated data points and transmitted to a large language model (LLM) to prompt the LLM to automatically generate synthetic training data for training the ML model on the concept. Some of the data points in the synthetic training data are labeled to generate a set of labeled synthetic training data and the local ML model and the ML model are updated based on the set of labeled synthetic training data. A second prompt is then generated, based on the set of labeled synthetic training data and transmitted to the LLM to prompt the LLM to automatically generate an updated set of synthetic training data. The process of labeling the synthetic training data to generate a set of updated labeled training data, updating the local ML model and the ML model based on the set of updated labeled training data; generating another prompt based on the updated labeled training data, transmitting the second prompt to the LLM and receiving from the LLM another updated set of synthetic training data is repeated until the ML model no longer disagrees with the local model with respect to the concept.