InterviewSim: A Scalable Framework for Interview-Grounded Personality Simulation
This research tested methods for simulating real personalities using large language models by grounding them in actual interview data rather than just profiles. They created a framework to evaluate these simulations using four metrics: content similarity, factual consistency, personality alignment, and knowledge retention. The findings show that methods based on chronological interview sequences are best for preserving facts and structure.