LLM Chatbot Validation via Customer Digital Twin Simulations
A novel approach employs high-fidelity synthetic customer agents (SCAs) as digital counterparts to assess large-scale LLM-driven chatbots in regulated sectors such as banking. These SCAs are based on authentic transactional and conversational data, allowing for the automatic creation and conditioning of behaviors to mimic various customer profiles and interaction styles. Assessments indicate strong semantic alignment with actual customers, minimal hallucination occurrences, and effective reproduction of personality traits. The validation framework integrates automated LLM-as-a-Judge evaluations, expert human testing, and adversarial probing, which includes scenario-based validation across different emotional states and demographic categories.
Key facts
- LLM-based chatbots are transforming customer service in regulated domains such as banking.
- Scalable and cost-effective validation remains a critical barrier to safe deployment.
- The methodology creates high-fidelity synthetic customer agents (SCAs) as digital twins.
- SCAs are grounded in real transactional and conversational data.
- SCAs enable automatic generation and behavioral conditioning to simulate diverse customer profiles and interaction styles.
- SCAs achieve high semantic alignment with real customers, low hallucination rates, and successful personality trait reproduction.
- The validation framework combines automated LLM-as-a-Judge evaluation, human expert testing, and adversarial probing.
- Scenario-based validation covers emotional states and demographic groups.
Entities
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