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Memorization in Large Language Models (LLMs) poses privacy and security risks, as models may unintentionally reproduce sensitive or copyrighted data.
An Introduction to the Bootstrap
Efron, B. and Tibshirani, R. J · 1994
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Rouge: A package for automatic evaluation of summaries
Lin, C.-Y · 2004
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A normalized levenshtein distance metric
Yujian, L. and Bo, L · 2007
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Understanding machine learning: From theory to algorithms
Shalev-Shwartz, S. and Ben-David, S · 2014
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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Does learning require memorization? a short tale about a long tail
Feldman, V · 2020
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Scaling laws for neural language models
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D · 2020
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Extracting training data from large language models
Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., et al · 2021
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Understanding deep learning (still) requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O · 2021
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Quantifying memorization across neural language models
Carlini, N., Ippolito, D., Jagielski, M., Lee, K., Tramer, F., and Zhang, C · 2022
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Training compute-optimal large language models
Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. d. L., Hendricks, L. A., Welbl, J., Clark, A., et al · 2022
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Measuring forgetting of memorized training examples
Jagielski, M., Thakkar, O., Tramer, F., Ippolito, D., Lee, K., Carlini, N., Wallace, E., Song, S., Thakurta, A., Papernot, N., et al · 2022
Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A., et al · 2024
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Rethinking llm memorization through the lens of adversarial compression
Schwarzschild, A., Feng, Z., Maini, P., Lipton, Z. C., and Kolter, J. Z · 2024
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Healthq: Unveiling questioning capabilities of llm chains in healthcare conversations
Wang, Z., Li, H., Huang, D., and Rahmani, A. M · 2024
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On memorization of large language models in logical reasoning
Xie, C., Huang, Y., Zhang, C., Yu, D., Chen, X., Lin, B. Y., Li, B., Ghazi, B., and Kumar, R · 2024
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Memorization without overfitting: Analyzing the training dynamics of large language models
Tirumala, K., Markosyan, A., Zettlemoyer, L., and Aghajanyan, A · 2022
Cited alongside, same era.
Efficient memory management for large language model serving with pagedattention
Kwon, W., Li, Z., Zhuang, S., Sheng, Y., Zheng, L., Yu, C. H., Gonzalez, J. E., Zhang, H., and Stoica, I · 2023
Cited alongside, same era.
Scalable neural network kernels
Sehanobish, A., Choromanski, K. M., ZHAO, Y., Dubey, K. A., and Likhosherstov, V
Cited in the paper.
Zhao, Y., Behari, N., Hughes, E., Zhang, E., Nagaraj, D., Tuyls, K., Taneja, A., and Tambe, M · 2024
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Llamafactory: Unified efficient fine-tuning of 100+ language models
Zheng, Y., Zhang, R., Zhang, J., Ye, Y., Luo, Z., Feng, Z., and Ma, Y · 2024
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