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

TL;DR

This paper presents a framework to detect and evaluate the presence of copyrighted content in large language model training datasets, aiming to promote ethical data use and transparency.

Contribution

It introduces a novel framework for identifying copyrighted material in LLM datasets and estimating the confidence of each detection, validated through simulated experiments.

Findings

Effective detection of copyrighted content in datasets

Identification of literary quotes in training data

Implications for ethical data management in LLM development

Abstract

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.