Abstract: Techniques are described herein for a method of generating a synthetic chat between a customer module and an agent module, wherein: the customer module receives a first prompt and determines a first chat response, and the agent module receives a second prompt and determines a second chat response; generating, by a summarizer module, a summary of the synthetic chat; scoring, by a scorer module, the synthetic chat by comparing the summary of the synthetic chat to the first prompt and the second prompt; adjusting, based on the score, a parameter associated with the synthetic chat.

Abstract: Techniques are described herein for a method of generating a synthetic chat between a customer module and an agent module, wherein: the customer module receives a first prompt and determines a first chat response, and the agent module receives a second prompt and determines a second chat response; generating, by a summarizer module, a summary of the synthetic chat; scoring, by a scorer module, the synthetic chat by comparing the summary of the synthetic chat to the first prompt and the second prompt; adjusting, based on the score, a parameter associated with the synthetic chat.

Abstract: Techniques are described herein for a method of determining a similarity of each neuron in a layer of neurons of a neural network model to each other neuron in the layer of neurons. The method further comprises determining a redundant set of neurons and a non-redundant set of neurons based on the similarity of each neuron in the layer. The method further comprises fine tuning the set of non-redundant neurons using a first set of training data. The method further comprises training the set of redundant neurons using a second set of training data.