ConvoGen: Enhancing Conversational AI with Synthetic Data: A Multi-Agent Approach
This work addresses the need for diverse and realistic synthetic data to enhance conversational AI development, though it appears incremental as it builds on existing few-shot and multi-agent methods.
The paper tackled the problem of generating synthetic conversational data by introducing ConvoGen, a multi-agent framework that uses few-shot learning and iterative sampling, resulting in high-quality diverse data for training and evaluating conversational AI models.
In this paper, we present ConvoGen: an innovative framework for generating synthetic conversational data using multi-agent systems. Our method leverages few-shot learning and introduces iterative sampling from a dynamically updated few-shot hub to create diverse and realistic conversational scenarios. The generated data has numerous applications, including training and evaluating conversational AI models, and augmenting existing datasets for tasks like conversational intent classification or conversation summarization. Our experiments demonstrate the effectiveness of this method in producing high-quality diverse synthetic conversational data, highlighting its potential to enhance the development and evaluation of conversational AI systems.