Be.FM: Open Foundation Models for Human Behavior
Fuente:
arXiv
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| Auteurs principaux: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915311881551872 |
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| author | Xie, Yutong Li, Zhuoheng Wang, Xiyuan Pan, Yijun Liu, Qijia Cui, Xingzhi Lo, Kuang-Yu Gao, Ruoyi Zhang, Xingjian Huang, Jin Yuan, Walter Jackson, Matthew O. Mei, Qiaozhu |
| author_facet | Xie, Yutong Li, Zhuoheng Wang, Xiyuan Pan, Yijun Liu, Qijia Cui, Xingzhi Lo, Kuang-Yu Gao, Ruoyi Zhang, Xingjian Huang, Jin Yuan, Walter Jackson, Matthew O. Mei, Qiaozhu |
| contents | Despite their success in numerous fields, the potential of foundation models for modeling and understanding human behavior remains largely unexplored. We introduce Be.FM, one of the first open foundation models designed for human behavior modeling. Built upon open-source large language models and fine-tuned on a diverse range of behavioral data, Be.FM can be used to understand and predict human decision-making. We construct a comprehensive set of benchmark tasks for testing the capabilities of behavioral foundation models. Our results demonstrate that Be.FM can predict behaviors, infer characteristics of individuals and populations, generate insights about contexts, and apply behavioral science knowledge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_23058 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Be.FM: Open Foundation Models for Human Behavior Xie, Yutong Li, Zhuoheng Wang, Xiyuan Pan, Yijun Liu, Qijia Cui, Xingzhi Lo, Kuang-Yu Gao, Ruoyi Zhang, Xingjian Huang, Jin Yuan, Walter Jackson, Matthew O. Mei, Qiaozhu Artificial Intelligence Computational Engineering, Finance, and Science Computation and Language Despite their success in numerous fields, the potential of foundation models for modeling and understanding human behavior remains largely unexplored. We introduce Be.FM, one of the first open foundation models designed for human behavior modeling. Built upon open-source large language models and fine-tuned on a diverse range of behavioral data, Be.FM can be used to understand and predict human decision-making. We construct a comprehensive set of benchmark tasks for testing the capabilities of behavioral foundation models. Our results demonstrate that Be.FM can predict behaviors, infer characteristics of individuals and populations, generate insights about contexts, and apply behavioral science knowledge. |
| title | Be.FM: Open Foundation Models for Human Behavior |
| topic | Artificial Intelligence Computational Engineering, Finance, and Science Computation and Language |
| url | https://arxiv.org/abs/2505.23058 |