--- title: "Who cites Emergent and Predictable — 94 citing papers" description: "94 papers of the Elamur Research library cite «Emergent and Predictable Memorization in Large Language Models», found in their…" canonical: "https://research.elamur.ai/free/en/papers/biderman-2023-emergent-predictable-memorization-large-language/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/biderman-2023-emergent-predictable-memorization-large-language/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Emergent and Predictable Memorization in Large…](https://md.research.elamur.ai/free/en/papers/biderman-2023-emergent-predictable-memorization-large-language/) / cited by # Who cites «Emergent and Predictable Memorization in Large Language Models» 94 papers of our library cite it (of the 149,439 whose bibliographies we read) · 16 with a page here, the most cited first · [← back to «Emergent and Predictable Memorization in Large Language Models»](https://md.research.elamur.ai/free/en/papers/biderman-2023-emergent-predictable-memorization-large-language/) 1. [**Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context arXiv:2403.05530**Gemini Team, Petko Georgiev, Ving Ian Lei, et al.*2024 · 1,589 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2024-gemini-1-5-unlocking-multimodal/) 2. [**PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts arXiv:2306.04528**Kaijie Zhu, Jindong Wang, Jiaheng Zhou, et al.*2023 · 305 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-promptrobust-towards-evaluating-robustness-large/) 3. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/) 4. **[Large Language Models Are Zero-Shot Time Series Forecasters](https://md.research.elamur.ai/free/en/papers/gruver-2023-large-language-models-zero-shot/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Nate Gruver, Marc Finzi, Shikai Qiu, et al.*2023 · 268 citations* 5. **[Hallucinations in Large Multilingual Translation Models](https://md.research.elamur.ai/free/en/papers/guerreiro-2023-hallucinations-large-multilingual-translation-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Nuno M. Guerreiro, Duarte M. Alves, Jonas Waldendorf, et al.*2023 · 244 citations* 6. [**Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing arXiv:2406.08464**Zhangchen Xu, Fengqing Jiang, Luyao Niu, et al.*2024 · 148 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-magpie-alignment-data-synthesis-scratch/) 7. [**A Survey on Data Selection for Language Models arXiv:2402.16827**Alon Albalak, Yanai Elazar, Sang Michael Xie, et al.*2024 · 95 citations*](https://md.research.elamur.ai/free/en/papers/albalak-2024-survey-data-selection-language-models/) 8. [**Do Membership Inference Attacks Work on Large Language Models? arXiv:2402.07841**Michael Duan, Anshuman Suri, Niloofar Mireshghallah, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/duan-2024-membership-inference-attacks-work-large/) 9. **[In-Context Learning with Long-Context Models: An In-Depth Exploration](https://md.research.elamur.ai/free/en/papers/bertsch-2025-context-learning-long-context-models/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Amanda Bertsch, Maor Ivgi, Emily Xiao, et al.*2025 · 81 citations* 10. [**Rho-1: Not All Tokens Are What You Need arXiv:2404.07965**Zhenghao Lin, Zhibin Gou, Yeyun Gong, et al.*2024 · 59 citations*](https://md.research.elamur.ai/free/en/papers/lin-2024-rho-1-all-tokens-need/) 11. **[On Memorization of Large Language Models in Logical Reasoning](https://md.research.elamur.ai/free/en/papers/xie-2025-memorization-large-language-models-logical/) [International Joint Conference on Natural Language Processing (IJCNLP)](https://md.research.elamur.ai/free/en/venues/ijcnlp/)**Chulin Xie, Yangsibo Huang, Chiyuan Zhang, et al.*2025 · 45 citations* 12. **[DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning Graph](https://md.research.elamur.ai/free/en/papers/zhang-2024-darg-dynamic-evaluation-large-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhehao Zhang, Jiaao Chen, Diyi Yang*2024 · 17 citations* 13. **[Beyond Math: Stories as a Testbed for Memorization-Constrained Reasoning in LLMs](https://md.research.elamur.ai/free/en/papers/jiang-2026-beyond-math-stories-as-testbed/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Yuxuan Jiang, Francis Ferraro*2026 · 10 citations* 14. **[Are LLM Benchmarks Already Contaminated? A Systematic Review of Contamination Detection Methods](https://md.research.elamur.ai/free/en/papers/nourbakhsh-2026-are-llm-benchmarks-already-contaminated/) [Workshop on Generation, Evaluation and Metrics (GEM)](https://md.research.elamur.ai/free/en/venues/gem/)**Erfan Nourbakhsh, Mohammad Sadegh Sirjani, Amir Mousavi, et al.*2026* 15. **[Exploring Cross-Client Memorization of Training Data in Large Language Models for Federated Learning](https://md.research.elamur.ai/free/en/papers/udsa-2026-exploring-cross-client-memorization-training/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tinnakit Udsa, Can Udomcharoenchaikit, Patomporn Payoungkhamdee, et al.*2026* 16. **[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* 78 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/biderman-2023-emergent-predictable-memorization-large-language/) — Emergent and Predictable Memorization in Large Language Models