Large Language Model-Based Role-Play Simulation
Overview
This field studies how language models can simulate people and characters in interactive settings, from fictional worlds and customer conversations to clinical, educational, and social scenarios. Common approaches combine persona prompts with dialogue memory, psychological or knowledge-state models, multi-agent designs, and fine-tuning or reinforcement learning to shape behavior over multiple turns. Researchers evaluate realism, persona consistency, task outcomes, and safety using human judgments, dialogue benchmarks, and comparisons with real-world behavior. A central challenge is that fluent role-play can still fail to reflect a person's knowledge, motivations, or boundaries, while longer interactions can expose drift, bias, and unsafe responses.
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