这究竟是谁的文本？探讨BigCode、知识产权与伦理
Whose Text Is It Anyway? Exploring BigCode, Intellectual Property, and Ethics
AI总结：
本文探讨在开放数据集上训练的LLMs如何规避数据版权利益，以GitHub Copilot为案例研究软件版权挑战，并为开发者和法律专家提供版权分析路线图。
摘要：
智能或生成式写作工具依赖于能够识别、摘要、翻译和预测内容的大型语言模型。本立场文件探讨了用于训练大型语言模型（LLMs）的开放数据集的版权利益。我们的论文提出疑问：在开放数据集上训练的LLMs如何规避所用数据的版权利益？我们首先定义软件版权并追溯其历史。我们以GitHub Copilot作为挑战软件版权的现代案例研究。我们的结论概述了生成式写作助手给版权带来的障碍，并为开发者、软件法律专家和普通用户提供了一份实用的版权分析路线图，供其在智能LLM驱动的写作工具背景下加以考虑。
英文摘要：
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.
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。
