LLMs Plagiarize: Ensuring Responsible Sourcing of Large Language Model Training Data Through Knowledge Graph Comparison
<|reference_start|>LLMs Plagiarize: Ensuring Responsible Sourcing of Large Language Model Training Data Through Knowledge Graph Comparison: In light of recent legal allegations brought by publishers, newspapers, and other creators of copyrighted corpora against large language model developers who use their copyrighted materials for training or fine-tuning purposes, we propose a novel system, a variant of a plagiarism detection system, that assesses whether a knowledge source has been used in the training or fine-tuning of a large language model. Unlike current methods, we utilize an approach that uses Resource Description Framework (RDF) triples to create knowledge graphs from both a source document and an LLM continuation of that document. These graphs are then analyzed with respect to content using cosine similarity and with respect to structure using a normalized version of graph edit distance that shows the degree of isomorphism. Unlike traditional plagiarism systems that focus on content matching and keyword identification between a source and a target corpus, our approach enables a broader and more accurate evaluation of similarity between a source document and LLM continuation by focusing on relationships between ideas and their organization with regards to others. Additionally, our approach does not require access to LLM metrics like perplexity that may be unavailable in closed large language model "black-box" systems, as well as the training corpus. We thus assess whether an LLM has "plagiarized" a corpus in its continuation through similarity measures. A prototype of our system will be found on a hyperlinked GitHub repository.<|reference_end|>
@article{mondal2024llms,
    title={LLMs Plagiarize: Ensuring Responsible Sourcing of Large Language Model
  Training Data Through Knowledge Graph Comparison},
    author={Devam Mondal, Carlo Lipizzi},
    journal={arXiv preprint arXiv:2407.02659},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02659},
    primaryClass={cs.CL cs.LG}
}