Abstract

Techniques for generating synthetic data for machine learning (ML) models are described. A system includes a language model that processes a task and a corresponding set of example inputs to generate another input, referred to herein as a machine-generated data. The machine-generated data is processed using a ML, model (that data is being generated for) to determine a model output, and the model output is analyzed to determine whether it corresponds to a target output. If the model output corresponds to the target output, then the machine-generated data is added to the set of example inputs and one of the original example inputs is removed to generate an updated set of example inputs. The updated set can be used for various training techniques.

Date

December 19, 2024

Authors

R Gupta, N Mehrabi, P Goyal, K Chang, A Galstyan

Inventors

Rahul Gupta, Ninareh Mehrabi, Palash Goyal, Kai-Wei Chang, Aram Galstyan

Patent_office

US

Application_number

18216271