Large-Scale ChatBot Validation Through Customer Digital Twin Simulations
Researchers propose using synthetic customer agents as digital twins to validate LLM-based chatbots at scale.
The methodology uses high-fidelity synthetic customer agents (SCAs) grounded in real transactional data to simulate interactions. This approach aims to provide a cost-effective and scalable way to test chatbot performance and safety in regulated environments like banking.