“DE-COP: Detecting Copyrighted Content in Language Models Training Data”, 2024-02-15 (; similar):
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 was included in training. DE-COP’s core approach is to probe a large language model (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 surpasses the prior best method by 9.6% in detection performance (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 ~4% accuracy.
The code and datasets are available at Github.