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This paper proposes Equation-to-Behavior Prompting and reinforcement learning to guide large language models to simulate diverse human decision-making patterns in persuasion games, showing improved belief accuracy and training outcomes.
Edu-Theater is a data-efficient agent framework that uses LLM-powered generative agents to simulate learner behavior in educational settings. It employs a cohort-aware roll-call paradigm to infer learner states with fewer data and computational resources, achieving higher simulation accuracy.
OdysSim presents a systematic investigation into behavioral foundation models for simulating human behavior, introducing the Soul taxonomy, a corpus of 21.4M interactions, and a training recipe that achieves state-of-the-art on 8 of 23 benchmark tasks while producing more human-like outputs.