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Who cites «What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation»

83 papers of our library cite it (of the 149,439 whose bibliographies we read) · 19 with a page here, the most cited first · ← back to «What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation»

  1. Extracting Training Data from Large Language ModelsarXiv:2012.07805Nicholas Carlini, Florian Tramer, Eric Wallace, et al.2020 · 955 citations
  2. Text Embeddings by Weakly-Supervised Contrastive Pre-trainingarXiv:2212.03533Liang Wang, Nan Yang, Xiaolong Huang, et al.2022 · 598 citations
  3. Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!arXiv:2310.03693Xiangyu Qi, Yi Zeng, Tinghao Xie, et al.2023 · 355 citations
  4. Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' AlignmentarXiv:2308.05374Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li2023 · 223 citations
  5. LESS: Selecting Influential Data for Targeted Instruction TuningarXiv:2402.04333Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, et al.2024 · 178 citations
  6. Deduplicating Training Data Mitigates Privacy Risks in Language ModelsarXiv:2202.06539Nikhil Kandpal, Eric Wallace, Colin Raffel2022 · 172 citations
  7. SemDeDup: Data-efficient learning at web-scale through semantic deduplicationarXiv:2303.09540Amro Abbas, Kushal Tirumala, Dániel Simig, et al.2023 · 108 citations
  8. Studying Large Language Model Generalization with Influence FunctionsarXiv:2308.03296Roger Grosse, Juhan Bae, Cem Anil, et al.2023 · 105 citations

64 more papers of our library cite it; they have no page here yet.

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