OdysSim: Building Foundation Models for Human Behavior Simulation
📰 ArXiv cs.AI
Learn how OdysSim builds foundation models for human behavior simulation, addressing the Sim2Real gap in large language models
Action Steps
- Build a taxonomy of human behavior using SOUL, a framework with five dimensions
- Train a foundation model using OdysSim to simulate human behavior at scale
- Evaluate the model's performance using interactive evaluation and social simulation metrics
- Compare the results with existing large language models to identify the Sim2Real gap
- Apply the insights from OdysSim to improve the helpfulness and realism of human simulators
Who Needs to Know This
AI researchers and engineers working on human behavior simulation and large language models can benefit from this research, as it provides a systematic investigation of behavioral foundation models
Key Insight
💡 OdysSim's SOUL taxonomy and foundation models can help address the Sim2Real gap in large language models, making them more realistic and helpful in human simulation tasks
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🤖 OdysSim: Building foundation models for human behavior simulation to bridge the Sim2Real gap in large language models #AI #HumanBehaviorSimulation
Key Takeaways
Learn how OdysSim builds foundation models for human behavior simulation, addressing the Sim2Real gap in large language models
Full Article
Title: OdysSim: Building Foundation Models for Human Behavior Simulation
Abstract:
arXiv:2606.14199v1 Announce Type: cross Abstract: Large language models are increasingly deployed as human simulators for interactive evaluation and social simulation. Yet helpfulness-driven post-training pulls them toward a homogeneous, overly agreeable assistant register, creating a behavioral Sim2Real gap. We present OdysSim, the largest open systematic investigation of behavioral foundation models, i.e., models trained to simulate human behavior at scale. We propose SOUL, a taxonomy of five
Abstract:
arXiv:2606.14199v1 Announce Type: cross Abstract: Large language models are increasingly deployed as human simulators for interactive evaluation and social simulation. Yet helpfulness-driven post-training pulls them toward a homogeneous, overly agreeable assistant register, creating a behavioral Sim2Real gap. We present OdysSim, the largest open systematic investigation of behavioral foundation models, i.e., models trained to simulate human behavior at scale. We propose SOUL, a taxonomy of five
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