Public release of the weights of pretrained foundation models, otherwise known as downloadable access \citep{solaiman_gradient_2023}, enables fine-tuning without the prohibitive expense of pretraining. Our work argues that increasingly accessible fine-tuning of downloadable models may increase hazards. First, we highlight research to improve the accessibility of fine-tuning. We split our discussion into research that A) reduces the computational cost of fine-tuning and B) improves the ability to share that cost across more actors. Second, we argue that increasingly accessible fine-tuning methods may increase hazard through facilitating malicious use and making oversight of models with potentially dangerous capabilities more difficult. Third, we discuss potential mitigatory measures, as well as benefits of more accessible fine-tuning. Given substantial remaining uncertainty about hazards, we conclude by emphasizing the urgent need for the development of mitigations.

翻译：预训练基础模型权重的公开发布（即可下载访问 \citep{solaiman_gradient_2023}）使得在无需高昂预训练成本的情况下进行微调成为可能。我们的工作指出，可下载模型日益便捷的微调可能会增加危害。首先，我们重点介绍了提升微调可及性的研究，并将讨论分为两类：A）降低微调计算成本的研究，以及B）提升在更多参与者之间分担该成本能力的研究。其次，我们论证了日益便捷的微调方法可能通过助长恶意使用、使对具有潜在危险能力的模型的监管更加困难，从而增加危害。第三，我们讨论了潜在的缓解措施以及更便捷微调带来的益处。鉴于危害仍存在较大不确定性，我们最后强调亟需制定缓解方案。