Current regulations on powerful AI capabilities are narrowly focused on "foundation" or "frontier" models. However, these terms are vague and inconsistently defined, leading to an unstable foundation for governance efforts. Critically, policy debates often fail to consider the data used with these models, despite the clear link between data and model performance. Even (relatively) "small" models that fall outside the typical definitions of foundation and frontier models can achieve equivalent outcomes when exposed to sufficiently specific datasets. In this work, we illustrate the importance of considering dataset size and content as essential factors in assessing the risks posed by models both today and in the future. More broadly, we emphasize the risk posed by over-regulating reactively and provide a path towards careful, quantitative evaluation of capabilities that can lead to a simplified regulatory environment.

翻译：当前针对强大人工智能能力的监管主要狭隘地聚焦于"基础"或"前沿"模型。然而，这些术语定义模糊且不一致，导致治理工作缺乏稳定基础。关键在于，政策辩论往往未能考虑这些模型所使用的数据，尽管数据与模型性能之间存在明确关联。即使（相对）"小型"模型不符合基础和前沿模型的典型定义，当接触足够特定的数据集时，也能实现同等效果。本研究通过实证分析表明，在评估当前及未来模型带来的风险时，必须将数据集规模与内容作为核心考量因素。更广泛而言，我们强调被动式过度监管带来的风险，并提出通过严谨的量化能力评估路径，为构建简化的监管环境提供可行方案。