Be.FM: Open Foundation Models for Human Behavior
Yutong Xie, Zhuoheng Li, Xiyuan Wang, Yijun Pan, Qijia Liu, Xingzhi Cui, Kuang-Yu Lo, Ruoyi Gao, Xingjian Zhang, Jin Huang, Walter Yuan, Matthew O. Jackson, Qiaozhu Mei
TL;DR
Be.FM is an open foundation model tailored for human behavior understanding, capable of predicting decisions, inferring characteristics, and generating behavioral insights, advancing AI's role in behavioral science.
Contribution
It introduces one of the first open foundation models specifically designed for modeling and understanding human behavior, fine-tuned on diverse behavioral data.
Findings
Be.FM can predict human behaviors accurately.
It infers individual and population characteristics.
It generates meaningful insights about behavioral contexts.
Abstract
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsBehavioral and Psychological Studies · Digital Mental Health Interventions · Primate Behavior and Ecology
MethodsSparse Evolutionary Training