開源高性能基礎模型：風險、好處和追求開源目標的替代方法評估

摘要

近期一些領先的人工智慧實驗室決定開源他們的模型或限制對模型的訪問，引發了關於日益強大的人工智慧模型應該如何共享的爭論。在人工智慧領域，開源通常指的是將模型結構和權重公開並免費提供給任何人進行修改、研究、擴展和使用。這樣做的優勢包括促進外部監督、加速進步以及分散人工智慧開發和應用的控制權。然而，這也帶來了潛在的濫用和意外後果。本文對開源高性能基礎模型的風險和好處進行了探討。雖然開源在歷史上對大多數軟件和人工智慧開發過程帶來了實質的淨利益，但我們認為對於未來可能開發的某些高性能基礎模型來說，開源可能存在足夠嚴重的風險，超過了好處。在這種情況下，高性能基礎模型應該不開源，至少最初不應該開源。本文探討了包括非開源模型共享在內的替代策略。最後，本文提出了針對開發人員、標準制定機構和政府建立安全和負責任的模型共享實踐，並在安全的情況下保留開源好處的建議。

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.

開源高性能基礎模型：風險、好處和追求開源目標的替代方法評估

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

摘要

Support