Digger: Detecting Copyright Content Mis-usage in Large Language Model Training
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arXiv
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| Format: | Preprint |
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2024
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| author | Li, Haodong Deng, Gelei Liu, Yi Wang, Kailong Li, Yuekang Zhang, Tianwei Liu, Yang Xu, Guoai Xu, Guosheng Wang, Haoyu |
| author_facet | Li, Haodong Deng, Gelei Liu, Yi Wang, Kailong Li, Yuekang Zhang, Tianwei Liu, Yang Xu, Guoai Xu, Guosheng Wang, Haoyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_00676 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Digger: Detecting Copyright Content Mis-usage in Large Language Model Training Li, Haodong Deng, Gelei Liu, Yi Wang, Kailong Li, Yuekang Zhang, Tianwei Liu, Yang Xu, Guoai Xu, Guosheng Wang, Haoyu Cryptography and Security Computation and Language Machine Learning 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. |
| title | Digger: Detecting Copyright Content Mis-usage in Large Language Model Training |
| topic | Cryptography and Security Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2401.00676 |