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HumanLM: Simulating Users with State Alignment Beats Response Imitation
arXiv – CS AI|Shirley Wu, Evelyn Choi, Arpandeep Khatua, Zhanghan Wang, Joy He-Yueya, Tharindu Cyril Weerasooriya, Wei Wei, Diyi Yang, Jure Leskovec, James Zou|
🤖AI Summary
Researchers introduce HumanLM, a novel AI training framework that creates user simulators by aligning psychological states rather than just imitating response patterns. The system achieved 16.3% improvement in alignment scores across six datasets with 26k users and 216k responses, demonstrating superior ability to simulate real human behavior.
Key Takeaways
- →HumanLM uses reinforcement learning to align natural-language latent states with ground-truth responses, capturing underlying user psychology.
- →The framework outperformed existing approaches with 16.3% relative improvement in alignment scores across comprehensive benchmarks.
- →Humanual benchmark includes six datasets spanning 26k users and 216k responses across diverse contexts from daily life to political discussions.
- →Real-time study with 111 participants confirmed HumanLM achieves highest similarity to actual user responses.
- →The approach moves beyond surface-level imitation to capture deeper psychological states like beliefs and emotions.
#humanlm#user-simulation#reinforcement-learning#psychological-modeling#llm#human-ai-interaction#arxiv#benchmark#state-alignment
Read Original →via arXiv – CS AI
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