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.

Abstract: A system and method and for method for optimizing performance of a natural language processing (NLP) model includes clustering a validation dataset used in training the NLP model into a plurality of clusters; measuring a generalization in context parameter for one or more of the plurality of clusters; measuring an interference in context parameter for one or more of the plurality of clusters; and identifying a cluster, from among the plurality of clusters, for data augmentation, based on the measured generalization in context parameter and the measured interference in context parameter. Once a cluster is identified, a prompt is generated for submission as an input to a large language model (LLM) to prompt the LLM to automatically generate synthetic training data for the identified cluster, before the prompt is provided to the LLM and synthetic training data is received from the LLM.

Abstract: A system and method and for method for optimizing performance of a natural language processing (NLP) model includes clustering a validation dataset used in training the NLP model into a plurality of clusters; measuring a generalization in context parameter for one or more of the plurality of clusters; measuring an interference in context parameter for one or more of the plurality of clusters; and identifying a cluster, from among the plurality of clusters, for data augmentation, based on the measured generalization in context parameter and the measured interference in context parameter. Once a cluster is identified, a prompt is generated for submission as an input to a large language model (LLM) to prompt the LLM to automatically generate synthetic training data for the identified cluster, before the prompt is provided to the LLM and synthetic training data is received from the LLM.

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.