[论文解读] Towards a Framework for Openness in Foundation Models: Proceedings from the Columbia Convening on Openness in Artificial Intelligence

Adrien Basdevant, Camille François+14 more|arXiv (Cornell University)|May 17, 2024

Law, AI, and Intellectual Property被引用 5

一句话总结

本文提出一个框架，用以应对基础模型AI栈中的开放性，概述了现有工作，分析追求开放性的原因，并概述开放性如何在模型层和系统层面变化以支持对微妙的安全定义。

ABSTRACT

Over the past year, there has been a robust debate about the benefits and risks of open sourcing foundation models. However, this discussion has often taken place at a high level of generality or with a narrow focus on specific technical attributes. In part, this is because defining open source for foundation models has proven tricky, given its significant differences from traditional software development. In order to inform more practical and nuanced decisions about opening AI systems, including foundation models, this paper presents a framework for grappling with openness across the AI stack. It summarizes previous work on this topic, analyzes the various potential reasons to pursue openness, and outlines how openness varies in different parts of the AI stack, both at the model and at the system level. In doing so, its authors hope to provide a common descriptive framework to deepen a nuanced and rigorous understanding of openness in AI and enable further work around definitions of openness and safety in AI.

研究动机与目标

• 提供一个描述性框架，用于理解AI系统中的开放性，超越传统软件开放性的范围。
• 总结关于基础模型开放性的既有研究，以巩固现有观点。
• 分析AI系统开放性的动机、潜在收益与风险。
• 解释开放性如何在AI栈的不同部分、在模型层和系统层面上变化。

提出的方法

• 回顾并综合现有关于AI和基础模型开放性的文献。
• 分析在AI系统中追求开放性的各种潜在原因。
• 概述开放性如何在AI栈中变化，包括模型层和系统层的考虑。

实验结果

研究问题

• RQ1 在AI栈中，基础模型的开放性由哪些要素构成？
• RQ2 开放性如何在模型层和系统层组件之间变化？
• RQ3 追求开放性的驱动因素有哪些，随之产生的安全性影响是什么？

主要发现

• 提供一个描述性框架来分析AI中的开放性，旨在实现对开放性和安全性的更细粒度定义。
• 总结既有工作，以为在开放AI系统时提供务实且细致的决策信息。
• 阐明开放性在AI栈的不同部分（包括模型层和系统层）如何变化。