Tag
This paper proposes a method to automatically generate diverse user personas using large language models for testing interview dialogue systems, reducing manual effort and increasing variation in simulated user behaviors.
This paper presents KnowSim, an evaluation framework that models user knowledge states to assess information calibration in LLM assistants, validated against human judgments and outperforming baseline simulators.
Introduces Persona Policies (PPol), a plug-and-play control layer that uses LLM-driven evolutionary program search to generate diverse, human-like user personas for evaluating LLM agents. Achieves 33–62% fitness gains over baseline, with human-likeness rated at 80.4%, and improves agent robustness with +17% task success.