开源模型Be.FM可预测人类行为并理解决策机制
Be.FM: Open Foundation Models for Human Behavior
- 基于开源大模型，用多样化行为数据微调
- 能预测行为、推断个体/群体特征、生成情境洞察
- 适合行为科学、心理学与人机交互研究者
尽管基础模型在众多领域取得成功，其在人类行为建模与理解方面的潜力仍待挖掘。我们提出Be.FM，首个面向人类行为建模的开源基础模型。该模型基于开源大语言模型，并在多样化的行为数据上进行微调，可用于理解与预测人类决策。我们构建了一套全面的基准任务，用于评估行为基础模型的能力。实验结果表明，Be.FM能够预测行为、推断个体与群体特征、生成对情境的洞察，并应用行为科学知识。
原文摘要 · Abstract (English)
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.
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