DE-COP: Detecting Copyrighted Content in Language Models Training Data

André Vicente Duarte, Xuandong Zhao, Arlindo L. Oliveira, Lei Li

Abstract: *How can we detect if copyrighted content was used in the training process of a language model, considering that the training data is typically undisclosed?* We are motivated by the premise that a language model is likely to identify verbatim excerpts from its training text. We propose DE-COP, a method to determine whether a piece of copyrighted content is included in training. DE-COP's core approach is to probe an LLM with multiple-choice questions, whose options include both verbatim text and their paraphrases. We construct BookTection, a benchmark with excerpts from 165 books published prior and subsequent to a model's training cutoff, along with their paraphrases. Our experiments show that DE-COP outperforms the prior best method by 8.6% in detection accuracy (AUC) on models with logits available. Moreover, DE-COP also achieves an average accuracy of 72% for detecting suspect books on fully black-box models where prior methods give approximately 0% accuracy. The code and datasets are available at https://github.com/LeiLiLab/DE-COP.

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Submission Number: 9355