Digger：检测大型语言模型训练中版权内容的滥用
Digger: Detecting Copyright Content Mis-usage in Large Language Model Training
AI总结：
本文提出Digger框架，通过检测LLMs训练数据集中的版权书籍内容并提供置信度估计，验证其有效性，以确保版权材料的道德使用并促进透明数据管理。
摘要：
预训练利用广泛且多样的数据集，是大型语言模型（LLMs）在众多应用中取得成功的关键因素。然而，这些数据集的详细构成往往未被披露，引发了关于数据安全和潜在滥用的担忧。当仍受法律保护的版权材料被不当使用时，无论是故意还是无意，都会侵犯作者权利，这一问题尤为突出。本文提出了一个详细的框架，旨在检测和评估LLMs训练数据集中是否包含潜在版权书籍的内容。该框架还为每个内容样本被包含的可能性提供了置信度估计。为验证我们的方法，我们进行了一系列模拟实验，结果证实了该框架在识别和处理LLM训练过程中内容滥用实例的有效性。此外，我们还调查了这些数据集中是否存在来自著名文学作品的知名引语。我们的研究结果对确保LLMs开发中版权材料的道德使用具有重要意义，突显了该领域需要更透明和负责任的数据管理实践。
英文摘要：
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.
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。
