Large-Scale ChatBot Validation Through Customer Digital Twin Simulations

Short summary

Researchers present a methodology for creating synthetic customer agents (SCAs) as digital twins, grounded in real transactional and conversational data, to validate LLM-based chatbots at scale. The framework combines LLM-as-a-Judge evaluation, human expert testing, and adversarial probing across emotional states, demographics, and linguistic factors. It was successfully applied to validate a customer-facing chatbot at a leading UK bank, offering financial institutions a scalable path to regulatory compliance.

• •Synthetic customer agents (SCAs) serve as digital twins for large-scale chatbot validation
• •Validation framework combines LLM-as-a-Judge, expert testing, and adversarial probing
• •Deployed at a leading UK bank for regulatory compliance testing

Generated with AI, which can make mistakes.