Generating Diverse Personas for User Simulators to Test Interview Dialogue Systems
Interview dialogue systems require extensive testing with diverse user behaviors, but manual persona creation is labor-intensive and costly The authors propose an LLM-based method for automatically generating diverse personas for user simulators Personality traits related to communication styles are explicitly assigned during persona generation to increase behavioral diversity Experiments demonstrate that the proposed method produces user simulator utterances with significantly greater variation
Analysis
TL;DR
- Interview dialogue systems require extensive testing with diverse user behaviors, but manual persona creation is labor-intensive and costly
- The authors propose an LLM-based method for automatically generating diverse personas for user simulators
- Personality traits related to communication styles are explicitly assigned during persona generation to increase behavioral diversity
- Experiments demonstrate that the proposed method produces user simulator utterances with significantly greater variation compared to conventional approaches
- This work addresses a gap in existing user simulators, which have primarily been designed for task-oriented dialogue rather than open-ended interview scenarios
Why It Matters
Testing dialogue systems with real humans is expensive and time-consuming, making user simulators essential for iterative development. This work is particularly relevant as interview dialogue systems gain traction in HR, healthcare, and education, where evaluating system performance across diverse user personalities is critical before real-world deployment.
Technical Details
- The method uses a large language model to automatically generate personas with structured personality traits, specifically focusing on communication style dimensions
- Unlike conventional user simulators designed for task-oriented dialogue (e.g., slot-filling, goal-driven conversations), this approach targets open-ended interview dialogue scenarios requiring nuanced behavioral simulation
- Personality traits are explicitly encoded as generation constraints to ensure diversity across simulated users rather than relying on uniform response patterns
- Evaluation is conducted through experimental comparison measuring utterance variation, demonstrating that LLM-generated personas produce more diverse communication styles than baseline approaches
Industry Insight
- Organizations building interview or conversational assessment systems should adopt automated persona generation to reduce testing costs and accelerate development cycles
- The emphasis on communication-style diversity suggests that future user simulators should move beyond task-completion metrics and incorporate behavioral variation as a key evaluation dimension
- As LLM-based simulation becomes more sophisticated, it may reduce the need for large-scale human testing in early development phases, though human validation will remain essential for high-stakes interview applications
Disclaimer: The above content is generated by AI and is for reference only.