The paper "DIGGER: Detecting Copyright Content Mis-usage in Large Language Model Training" addresses the issue of copyright infringement in the training datasets of large language models (LLMs). The authors introduce a detailed framework called DIGGER to detect and assess the presence of content from potentially copyrighted books within LLM training datasets. The framework also provides a confidence estimation for the likelihood of each content sample's inclusion. The study begins with an empirical analysis of GPT-2, exploring how sample loss dynamics change during fine-tuning. Key findings include: 1. The discrepancy between inference results and original training data diminishes over time, plateauing. 2. Larger model architectures exhibit faster convergence during initial training epochs. 3. The inherent nature of training material influences loss, making it challenging to establish a universal loss criterion. Based on these findings, the authors propose DIGGER, which involves: 1. Generating a baseline dataset and fine-tuning a LLM for copyright detection. 2. Fine-tuning the target LLM with the target dataset without knowing its details. 3. Analyzing the sample loss difference between the reference LLM and the baseline LLM to differentiate between familiar and new content. 4. Calibrating the original LLM's loss distribution using the identified loss gap to derive a confidence score. The effectiveness of DIGGER is validated through controlled experiments and real-world LLM scenarios. Results show that DIGGER achieves an accuracy of 84.750% and a recall of 92.428% in identifying copyrighted content. The tool is made open-source to facilitate reproducibility and future research. The paper concludes by discussing the limitations and future directions, including computational costs, target probability calculation, and external threats such as limited confidence level calculation and copyright legal considerations. The work highlights the importance of transparent and responsible data management practices in the development of LLMs to ensure ethical use of copyrighted materials.The paper "DIGGER: Detecting Copyright Content Mis-usage in Large Language Model Training" addresses the issue of copyright infringement in the training datasets of large language models (LLMs). The authors introduce a detailed framework called DIGGER to detect and assess the presence of content from potentially copyrighted books within LLM training datasets. The framework also provides a confidence estimation for the likelihood of each content sample's inclusion. The study begins with an empirical analysis of GPT-2, exploring how sample loss dynamics change during fine-tuning. Key findings include: 1. The discrepancy between inference results and original training data diminishes over time, plateauing. 2. Larger model architectures exhibit faster convergence during initial training epochs. 3. The inherent nature of training material influences loss, making it challenging to establish a universal loss criterion. Based on these findings, the authors propose DIGGER, which involves: 1. Generating a baseline dataset and fine-tuning a LLM for copyright detection. 2. Fine-tuning the target LLM with the target dataset without knowing its details. 3. Analyzing the sample loss difference between the reference LLM and the baseline LLM to differentiate between familiar and new content. 4. Calibrating the original LLM's loss distribution using the identified loss gap to derive a confidence score. The effectiveness of DIGGER is validated through controlled experiments and real-world LLM scenarios. Results show that DIGGER achieves an accuracy of 84.750% and a recall of 92.428% in identifying copyrighted content. The tool is made open-source to facilitate reproducibility and future research. The paper concludes by discussing the limitations and future directions, including computational costs, target probability calculation, and external threats such as limited confidence level calculation and copyright legal considerations. The work highlights the importance of transparent and responsible data management practices in the development of LLMs to ensure ethical use of copyrighted materials.