🔗 Who cites «Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets»

100 papers of our library cite it (of the 149,439 whose bibliographies we read) · 27 with a page here, the most cited first · ← back to «Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets»

1. A Survey of Large Language ModelsFrontiers of Computer ScienceWayne Xin Zhao, Kun Zhou, Junyi Li, et al.2023 · 2,875 citations
2. In-context Learning and Induction HeadsarXiv:2209.11895Catherine Olsson, Nelson Elhage, Neel Nanda, et al.2022 · 475 citations
3. The AI Scientist: Towards Fully Automated Open-Ended Scientific DiscoveryarXiv:2408.06292Chris Lu, Cong Lu, Robert Tjarko Lange, et al.2024 · 372 citations
4. Faith and Fate: Limits of Transformers on CompositionalityConference on Neural Information Processing Systems (NeurIPS)Nouha Dziri, Ximing Lu, Melanie Sclar, et al.2023 · 327 citations
5. An Explanation of In-context Learning as Implicit Bayesian InferencearXiv:2111.02080Sang Michael Xie, Aditi Raghunathan, Percy Liang, et al.2021 · 286 citations
6. Predictability and Surprise in Large Generative ModelsACM Conference on Fairness, Accountability, and Transparency (FAccT)Deep Ganguli, Danny Hernandez, Liane Lovitt, et al.2022 · 268 citations
7. Transformers learn in-context by gradient descentarXiv:2212.07677Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, et al.2022 · 247 citations
8. Progress measures for grokking via mechanistic interpretabilityarXiv:2301.05217Neel Nanda, Lawrence Chan, Tom Lieberum, et al.2023 · 228 citations
9. AI Alignment: A Comprehensive SurveyarXiv:2310.19852Jiaming Ji, Tianyi Qiu, Boyuan Chen, et al.2023 · 173 citations
10. Unsolved Problems in ML SafetyarXiv:2109.13916Dan Hendrycks, Nicholas Carlini, John Schulman, et al.2021 · 151 citations
11. RAFT: Adapting Language Model to Domain Specific RAGarXiv:2403.10131Tianjun Zhang, Shishir G. Patil, Naman Jain, et al.2024 · 137 citations
12. Emergent and Predictable Memorization in Large Language ModelsConference on Neural Information Processing Systems (NeurIPS)Stella Biderman, USVSN Sai Prashanth, Lintang Sutawika, et al.2023 · 123 citations
13. Neural Networks and the Chomsky HierarchyarXiv:2207.02098Grégoire Delétang, Anian Ruoss, Jordi Grau-Moya, et al.2022 · 112 citations
14. Mechanistic Interpretability for AI Safety — A ReviewarXiv:2404.14082Leonard Bereska, Efstratios Gavves2024 · 108 citations
15. Studying Large Language Model Generalization with Influence FunctionsarXiv:2308.03296Roger Grosse, Juhan Bae, Cem Anil, et al.2023 · 105 citations
16. Reinforcement Learning for Reasoning in Large Language Models with One Training ExampleConference on Neural Information Processing Systems (NeurIPS)Yiping Wang, Qing Yang, Zhiyuan Zeng, et al.2025 · 87 citations
17. A Primer on the Inner Workings of Transformer-based Language ModelsarXiv:2405.00208Javier Ferrando, Gabriele Sarti, Arianna Bisazza, et al.2024 · 72 citations
18. Rho-1: Not All Tokens Are What You NeedarXiv:2404.07965Zhenghao Lin, Zhibin Gou, Yeyun Gong, et al.2024 · 59 citations
19. Zoology: Measuring and Improving Recall in Efficient Language ModelsarXiv:2312.04927Simran Arora; Sabri Eyuboglu; Aman Timalsina; Isys Johnson; Michael Poli; James Zou; Atri Rudra;, Christopher Re2023 · 55 citations
20. On Memorization of Large Language Models in Logical ReasoningInternational Joint Conference on Natural Language Processing (IJCNLP)Chulin Xie, Yangsibo Huang, Chiyuan Zhang, et al.2025 · 45 citations
21. Auxiliary task demands mask the capabilities of smaller language modelsarXiv:2404.02418Jennifer Hu, Michael C. Frank2024 · 41 citations
22. The Quantization Model of Neural ScalingarXiv:2303.13506Eric J. Michaud, Ziming Liu, Uzay Girit, et al.2023 · 30 citations
23. Pretraining on the Test Set Is All You NeedarXiv:2309.08632Rylan Schaeffer2023 · 24 citations
24. Do Transformers Grok Succinct Algorithms? Mechanistic Evidence for Counting CircuitsFindings of the Association for Computational LinguisticsYizheng Zhao2026
25. Memorization, Emergence, and Explaining Reversal Failures: A Controlled Study of Relational Semantics in LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Yihua Zhu, Qianying Liu, Jiaxin Wang, et al.2026
26. Memory Dial: A Training Framework for Controllable Memorization in Language ModelsFindings of the Association for Computational LinguisticsXiangbo Zhang, Ali Emami2026
27. Rethinking Data Mixing from the Perspective of Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Yuanjian Xu, Tianze Sun, Changwei Xu, et al.2026

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

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