--- title: "Who cites What Neural Networks Memorize — 83 citing papers" description: "83 papers of the Elamur Research library cite «What Neural Networks Memorize and Why: Discovering the Long Tail via Influence…" canonical: "https://research.elamur.ai/free/en/papers/feldman-2020-neural-networks-memorize-why-discovering/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/feldman-2020-neural-networks-memorize-why-discovering/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [What Neural Networks Memorize and Why…](https://md.research.elamur.ai/free/en/papers/feldman-2020-neural-networks-memorize-why-discovering/) / cited by # 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»](https://md.research.elamur.ai/free/en/papers/feldman-2020-neural-networks-memorize-why-discovering/) 1. [**Extracting Training Data from Large Language Models arXiv:2012.07805**Nicholas Carlini, Florian Tramer, Eric Wallace, et al.*2020 · 955 citations*](https://md.research.elamur.ai/free/en/papers/carlini-2020-extracting-training-data-large-language/) 2. [**Text Embeddings by Weakly-Supervised Contrastive Pre-training arXiv:2212.03533**Liang Wang, Nan Yang, Xiaolong Huang, et al.*2022 · 598 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-text-embeddings-weakly-supervised-contrastive/) 3. **[Membership Inference Attacks From First Principles](https://md.research.elamur.ai/free/en/papers/carlini-2021-membership-inference-attacks-first-principles/) [Proceedings - IEEE Symposium on Security and Privacy/Proceedings of the ... IEEE Symposium on Security and Privacy](https://md.research.elamur.ai/free/en/venues/proceedings-ieee-symposium-on-security-and-privacy-proceedings-of-the-ieee/)**Nicholas Carlini, Steve Chien, Milad Nasr, et al.*2021 · 584 citations* 4. [**Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To! arXiv:2310.03693**Xiangyu Qi, Yi Zeng, Tinghao Xie, et al.*2023 · 355 citations*](https://md.research.elamur.ai/free/en/papers/qi-2023-fine-tuning-aligned-language-models/) 5. **[The Curious Case of Hallucinations in Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/raunak-2021-curious-case-hallucinations-neural-machine/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Vikas Raunak, Arul Menezes, Marcin Junczys-Dowmunt*2021 · 241 citations* 6. **[Beyond neural scaling laws: beating power law scaling via data pruning](https://md.research.elamur.ai/free/en/papers/sorscher-2022-neural-scaling-laws-beating-power/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Ben Sorscher, Robert Geirhos, Shashank Shekhar, et al.*2022 · 231 citations* 7. [**Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment arXiv:2308.05374**Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li*2023 · 223 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-trustworthy-llms-survey-guideline-evaluating/) 8. **[Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models](https://md.research.elamur.ai/free/en/papers/tirumala-2022-memorization-overfitting-analyzing-training-dynamics/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Kushal Tirumala, Aram H. Markosyan, Luke Zettlemoyer, et al.*2022 · 190 citations* 9. [**LESS: Selecting Influential Data for Targeted Instruction Tuning arXiv:2402.04333**Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, et al.*2024 · 178 citations*](https://md.research.elamur.ai/free/en/papers/xia-2024-less-selecting-influential-data-targeted/) 10. [**Deduplicating Training Data Mitigates Privacy Risks in Language Models arXiv:2202.06539**Nikhil Kandpal, Eric Wallace, Colin Raffel*2022 · 172 citations*](https://md.research.elamur.ai/free/en/papers/kandpal-2022-deduplicating-training-data-mitigates-privacy/) 11. **[Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning](https://md.research.elamur.ai/free/en/papers/razeghi-2022-impact-pretraining-term-frequencies-few/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yasaman Razeghi, Robert L Logan IV, Matt Gardner, et al.*2022 · 165 citations* 12. [**SemDeDup: Data-efficient learning at web-scale through semantic deduplication arXiv:2303.09540**Amro Abbas, Kushal Tirumala, Dániel Simig, et al.*2023 · 108 citations*](https://md.research.elamur.ai/free/en/papers/abbas-2023-semdedup-data-efficient-learning-web/) 13. [**Studying Large Language Model Generalization with Influence Functions arXiv:2308.03296**Roger Grosse, Juhan Bae, Cem Anil, et al.*2023 · 105 citations*](https://md.research.elamur.ai/free/en/papers/grosse-2023-studying-large-language-model-generalization/) 14. **[D4: Improving LLM Pretraining via Document De-Duplication and Diversification](https://md.research.elamur.ai/free/en/papers/tirumala-2023-d4-improving-llm-pretraining-document/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Kushal Tirumala, Daniel Simig, Armen Aghajanyan, et al.*2023 · 81 citations* 15. **[Understanding Transformer Memorization Recall Through Idioms](https://md.research.elamur.ai/free/en/papers/haviv-2023-understanding-transformer-memorization-recall-through/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Adi Haviv, Ido Cohen, Jacob Gidron, et al.*2023 · 62 citations* 16. **[Future Lens: Anticipating Subsequent Tokens from a Single Hidden State](https://md.research.elamur.ai/free/en/papers/pal-2023-future-lens-anticipating-subsequent-tokens/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Koyena Pal, Jiuding Sun, Andrew Yuan, et al.*2023 · 49 citations* 17. **[DOSE: Data Selection for Multi-Modal LLMs via Off-the-Shelf Models](https://md.research.elamur.ai/free/en/papers/wu-2026-dose-data-selection-multi-modal/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Biao Wu, Yiwu Zhong, Meng Fang, et al.*2026* 18. **[Investigating Memorization in Language Models Trained via Knowledge Distillation](https://md.research.elamur.ai/free/en/papers/macking-2026-investigating-memorization-language-models-trained/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Maarten Mäcking, Michaela Regneri*2026* 19. **[Unintended Memorization of Sensitive Information in Fine-Tuned Language Models](https://md.research.elamur.ai/free/en/papers/szep-2026-unintended-memorization-sensitive-information-fine/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Marton Szep, Jorge Marin Ruiz, Georgios Kaissis, et al.*2026* 64 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/feldman-2020-neural-networks-memorize-why-discovering/) — What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation