# Digger: Detecting Copyright Content Mis-usage in Large Language Model Training

**Authors:** Haodong Li, Gelei Deng, Yi Liu, Kailong Wang, Yuekang Li, Tianwei Zhang, Yang Liu, Guoai Xu, Guosheng Xu, Haoyu Wang  
**arXiv:** [2401.00676](https://arxiv.org/abs/2401.00676)  
**Date:** 2024-01-01  
**Summary page:** https://www.summarizepaper.com/en/paper/2401.00676v1/

## Key points

- Authors address the issue of data security and potential misuse in Large Language Model (LLM) training
- Concerns about copyright infringement when copyrighted material is used without authorization
- Proposed framework to detect and assess potentially copyrighted content within LLM training datasets
- Framework provides confidence estimation for each content sample's likelihood of inclusion
- Simulated experiments demonstrate effectiveness in identifying and addressing instances of content misuse during LLM training processes
- Investigation of recognizable quotes from famous literary works within these datasets
- Importance of protecting authors' rights and preventing unintentional or intentional infringement emphasized

## Summary

In their paper titled "Digger: Detecting Copyright Content Mis-usage in Large Language Model Training," authors Haodong Li, Gelei Deng, Yi Liu, Kailong Wang, Yuekang Li, Tianwei Zhang, Yang Liu, Guoai Xu, Guosheng Xu and Haoyu Wang address the critical issue of data security and potential misuse in the pre-training process of Large Language Models (LLMs). Pre-training with extensive and varied datasets is crucial for the success of LLMs in various applications. However, the specific composition of these datasets is often undisclosed which raises concerns about copyright infringement when copyrighted material is used without authorization. To tackle this problem, the authors propose a detailed framework to detect and assess the presence of potentially copyrighted content within LLM training datasets. The framework also provides a confidence estimation for each content sample's likelihood of inclusion. To validate their approach, simulated experiments are conducted which demonstrate its effectiveness in identifying and addressing instances of content misuse during LLM training processes. Additionally, the study investigates the presence of recognizable quotes from famous literary works within these datasets. and  are major concerns addressed by this research as it highlights potential issues with using copyrighted materials without proper authorization during  development. The proposed framework offers a solution to ensure  by providing a comprehensive method for detecting copyright content misusage in LLM training. It also emphasizes the importance of  practices to protect authors' rights and prevent unintentional or intentional infringement.