Synthesizing dialogs for better conversational AI

This blogpost discusses the use of Gretel-GPT for generating realistic synthetic dialogs, turn-takings and QA datasets enhanced with metadata tags or labels. The purpose is to provide high-quality training data for natural language processing (NLP) and conversational AI models while preserving privacy. Three conversational datasets are demonstrated: Daily-dialog, Commonsense-Dialogues, and Counsel-chat. Gretel-GPT is a powerful tool that maintains the structure and order within a paragraph while generating text that sounds convincingly human. The model can be fine-tuned on structured conversational data to generate synthetic conversations enriched with metadata.