开放 ChatGPT：追踪指令微调文本生成器的开放性、透明度与问责性
Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generators
AI总结：
本文调研首批对标 ChatGPT 的开源指令微调 LLM，从代码、数据、权重、RLHF、许可、文档与访问方式评估其开放程度，指出许多所谓开源项目存在数据合法性、指令微调数据缺失和科学文档不足等问题。
摘要：
表现出指令遵循行为的大型语言模型代表了近期对话界面领域最大的剧变之一，这一趋势在很大程度上是由 OpenAI 发布的 ChatGPT 所推动的；ChatGPT 是一种专有的文本生成大型语言模型，通过基于人类反馈的强化学习（LLM+RLHF）进行微调。我们回顾了依赖专有软件的风险，并调研了第一批在架构和功能上具有可比性的开源项目。本文的主要贡献是表明开放性是有差异的，并为这一快速发展领域中的开放程度提供科学记录。我们从代码、训练数据、模型权重、RLHF 数据、许可、科学文档和访问方法的开放性方面评估相关项目。我们发现，尽管越来越多的项目自称“开源”，但许多项目继承了合法性存疑且缺乏文档记录的数据，很少有项目分享至关重要的指令微调数据（这是涉及人类标注劳动的关键环节），而严谨的科学文档极为罕见。从数据收集和整理到模型架构，从训练和微调到发布与部署，开放程度在所有环节都与公平性和问责性相关。
英文摘要：
Large language models that exhibit instruction-following behaviour represent one of the biggest recent upheavals in conversational interfaces, a trend in large part fuelled by the release of OpenAI's ChatGPT, a proprietary large language model for text generation fine-tuned through reinforcement learning from human feedback (LLM+RLHF). We review the risks of relying on proprietary software and survey the first crop of open-source projects of comparable architecture and functionality. The main contribution of this paper is to show that openness is differentiated, and to offer scientific documentation of degrees of openness in this fast-moving field. We evaluate projects in terms of openness of code, training data, model weights, RLHF data, licensing, scientific documentation, and access methods. We find that while there is a fast-growing list of projects billing themselves as 'open source', many inherit undocumented data of dubious legality, few share the all-important instruction-tuning (a key site where human annotation labour is involved), and careful scientific documentation is exceedingly rare. Degrees of openness are relevant to fairness and accountability at all points, from data collection and curation to model architecture, and from training and fine-tuning to release and deployment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
