Pre-training, which utilizes extensive and varied datasets, is a critical factor in the success of Large Language Models (LLMs) across numerous applications. However, the detailed makeup of these datasets is often not disclosed, leading to concerns about data security and potential misuse. This is particularly relevant when copyrighted material, still under legal protection, is used inappropriately, either intentionally or unintentionally, infringing on the rights of the authors. In this paper, we introduce a detailed framework designed to detect and assess the presence of content from potentially copyrighted books within the training datasets of LLMs. This framework also provides a confidence estimation for the likelihood of each content sample's inclusion. To validate our approach, we conduct a series of simulated experiments, the results of which affirm the framework's effectiveness in identifying and addressing instances of content misuse in LLM training processes. Furthermore, we investigate the presence of recognizable quotes from famous literary works within these datasets. The outcomes of our study have significant implications for ensuring the ethical use of copyrighted materials in the development of LLMs, highlighting the need for more transparent and responsible data management practices in this field.

翻译：预训练利用大规模多样化的数据集，是大语言模型（LLMs）在众多应用中取得成功的核心要素。然而，这些数据集的详细构成通常不予公开，引发了数据安全与潜在滥用的担忧。当受法律保护的版权材料被有意或无意地不当使用时，尤其会侵犯作者的权益。本文提出了一种专门框架，用于检测并评估受版权保护的书籍内容出现在大语言模型训练数据集中的情况。该框架同时提供了每份内容样本被纳入数据集的置信度估计。为验证方法有效性，我们开展了一系列模拟实验，结果证实了该框架在识别和处理大语言模型训练过程中内容滥用问题的有效性。此外，我们还探究了这些数据集中包含的名著作品可识别引文的存在情况。本研究的成果对确保大语言模型开发中版权材料的伦理使用具有重要意义，凸显了该领域亟需更透明、更负责任的数据管理实践。