Intelligent or generative writing tools rely on large language models that recognize, summarize, translate, and predict content. This position paper probes the copyright interests of open data sets used to train large language models (LLMs). Our paper asks, how do LLMs trained on open data sets circumvent the copyright interests of the used data? We start by defining software copyright and tracing its history. We rely on GitHub Copilot as a modern case study challenging software copyright. Our conclusion outlines obstacles that generative writing assistants create for copyright, and offers a practical road map for copyright analysis for developers, software law experts, and general users to consider in the context of intelligent LLM-powered writing tools.

翻译：智能或生成式写作工具依赖于能够识别、总结、翻译和预测内容的大型语言模型。本文立场论文探讨了用于训练大型语言模型（LLM）的开放数据集的版权问题。我们的研究提问：基于开放数据集训练的LLM如何规避所用数据的版权利益？我们首先对软件版权进行定义并追溯其历史，然后以GitHub Copilot作为现代案例，审视其对软件版权提出的挑战。结论部分概述了生成式写作助手为版权带来的障碍，并提出了一个实用的版权分析路线图，供开发者、软件法律专家及普通用户在智能LLM驱动的写作工具背景下参考。