The advancement of conversational AI systems relies on the availability of high-quality, flexible, and reproducible synthetic dialogues for training, evaluation, and benchmarking. SDialog is a modular, extensible Python toolkit designed to address the challenges of synthetic dialogue generation and analysis. By leveraging instruction-tuned Large Language Models (LLMs), SDialog provides abstractions for personas, orchestration, and scenario management, enabling the creation of realistic, diverse, and controllable conversational data for research and development. SDialog supports workflows such as multi-agent simulation and scenario-driven generation, and represents a step forward in the standardization of tools and frameworks for synthetic data generation, a crucial advancement for ensuring reproducibility in today's fast-evolving research landscape.
View on arXiv@article{burdisso2025_2506.10622,
title={ SDialog: A Python Toolkit for Synthetic Dialogue Generation and Analysis },
author={ Sergio Burdisso and Esaú Villatoro-Tello and Petr Motlicek },
journal={arXiv preprint arXiv:2506.10622},
year={ 2025 }
}