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The rise of Large Language Models (LLMs) has triggered legal and ethical concerns, especially regarding the unauthorized use of copyrighted materials in their training datasets.
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Lin, C.Y.: Rouge: A package for automatic evaluation of summaries. In: Text summarization branches out. pp. 74–81 (2004)
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Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: 2017 IEEE symposium on security and privacy (SP). pp. 3–18. IEEE (2017)
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Yeom, S., Giacomelli, I., Fredrikson, M., Jha, S.: Privacy risk in machine learning: Analyzing the connection to overfitting. In: 2018 IEEE 31st computer security foundations symposium (CSF). pp. 268–282. IEEE (2018)
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Gao, L., Biderman, S., Black, S., Golding, L., Hoppe, T., Foster, C., Phang, J., He, H., Thite, A., Nabeshima, N., Presser, S., Leahy, C.: The pile: An 800gb dataset of diverse text for language modeling (2020)
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Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., et al.: Extracting training data from large language models. In: 30th USENIX Security Symposium (USENIX Security 21). pp. 2633–2650 (2021)
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Biderman, S., Schoelkopf, H., Anthony, Q., Bradley, H., O’Brien, K., Hallahan, E., Khan, M.A., Purohit, S., Prashanth, U.S., Raff, E., Skowron, A., Sutawika, L., Van Der Wal, O.: Pythia: a suite for analyzing large language models across training and scaling. In: Proceedings of the 40th International Conference on Machine Learning. ICML’23, JMLR.org (2023)
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Computer, T.: Redpajama-data: An open source recipe to reproduce llama training dataset (2023), https://github.com/togethercomputer/RedPajama-Data
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Geng, X., Liu, H.: Openllama: An open reproduction of llama (May 2023), https://github.com/openlm-research/open_llama
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Groeneveld, D., Ha, C., Magnusson, I.: Bff: The big friendly filter (2023), https://github.com/allenai/bff
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2023
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2024
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2024
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Meeus, M., Jain, S., Rei, M., de Montjoye, Y.: Did the neurons read your book? document-level membership inference for large language models. In: Balzarotti, D., Xu, W. (eds.) 33rd USENIX Security Symposium, USENIX Security 2024, Philadelphia, PA, USA, August 14-16, 2024. USENIX Association (2024)
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Meeus, M., Shilov, I., Faysse, M., de Montjoye, Y.A.: Copyright traps for large language models. In: 41st International Conference on Machine Learning (2024)
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2024
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2024
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Jedrzejewski, F.V., Thode, L., Fischbach, J., Gorschek, T., Mendez, D., Lavesson, N.: Adversarial machine learning in industry: A systematic literature review. Computers & Security p. 103988 (2024)
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Shi, W., Ajith, A., Xia, M., Huang, Y., Liu, D., Blevins, T., Chen, D., Zettlemoyer, L.: Detecting pretraining data from large language models. In: The Twelfth International Conference on Learning Representations (2024)
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