开源高性能基础模型：风险、收益和追求开源目标的替代方法评估

摘要

最近，一些领先的人工智能实验室选择开源他们的模型，或者限制对模型的访问，引发了关于日益强大的人工智能模型是否应该共享，以及如何共享的讨论。在人工智能领域，开源通常指的是使模型的架构和权重可以自由公开地供任何人修改、研究、构建和使用。这样做的优势包括促进外部监督、加速进展，并分散对人工智能开发和使用的控制。然而，这也带来了越来越多的滥用和意外后果的潜在风险。本文对开源高度强大的基础模型的风险和好处进行了审视。虽然开源在历史上对大多数软件和人工智能开发过程提供了实质性的净益，但我们认为，对于未来可能开发的一些高度强大的基础模型，开源可能存在足够极端的风险，超过了好处。在这种情况下，高度强大的基础模型不应该被开源，至少最初不应该。本文探讨了包括非开源模型共享在内的替代策略。最后，本文提出了对开发者、标准制定机构和政府的建议，以建立安全和负责任的模型共享实践，并在安全的前提下保留开源的好处。

English

Recent decisions by leading AI labs to either open-source their models or to restrict access to their models has sparked debate about whether, and how, increasingly capable AI models should be shared. Open-sourcing in AI typically refers to making model architecture and weights freely and publicly accessible for anyone to modify, study, build on, and use. This offers advantages such as enabling external oversight, accelerating progress, and decentralizing control over AI development and use. However, it also presents a growing potential for misuse and unintended consequences. This paper offers an examination of the risks and benefits of open-sourcing highly capable foundation models. While open-sourcing has historically provided substantial net benefits for most software and AI development processes, we argue that for some highly capable foundation models likely to be developed in the near future, open-sourcing may pose sufficiently extreme risks to outweigh the benefits. In such a case, highly capable foundation models should not be open-sourced, at least not initially. Alternative strategies, including non-open-source model sharing options, are explored. The paper concludes with recommendations for developers, standard-setting bodies, and governments for establishing safe and responsible model sharing practices and preserving open-source benefits where safe.

开源高性能基础模型：风险、收益和追求开源目标的替代方法评估

Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives

摘要

Support