Be.FM: Open Foundation Models for Human Behavior
It addresses the problem of modeling human behavior for researchers and practitioners, but it appears incremental as it builds on existing open-source models and benchmarks.
The paper tackles the unexplored potential of foundation models for human behavior modeling by introducing Be.FM, an open foundation model fine-tuned on diverse behavioral data, which demonstrates capabilities in predicting behaviors, inferring characteristics, generating insights, and applying behavioral science knowledge.
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.