🔗 Who cites «A Constructive Prediction of the Generalization Error Across Scales»

52 papers of our library cite it (of the 149,439 whose bibliographies we read) · 17 with a page here, the most cited first · ← back to «A Constructive Prediction of the Generalization Error Across Scales»

1. Language Models are Few-Shot LearnersarXiv:2005.14165Tom B. Brown, Benjamin Mann, Nick Ryder, et al.2020 · 14,178 citations
2. LLaMA: Open and Efficient Foundation Language ModelsarXiv:2302.13971Hugo Touvron, Thibaut Lavril, Gautier Izacard, et al.2023 · 9,603 citations
3. Scaling Laws for Neural Language ModelsarXiv:2001.08361Jared Kaplan, Sam McCandlish, Tom Henighan, et al.2020 · 3,434 citations
4. Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelsarXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5BvosjAarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.2022 · 1,756 citations
5. Are Emergent Abilities of Large Language Models a Mirage?Conference on Neural Information Processing Systems (NeurIPS)Rylan Schaeffer, Brando Miranda, Sanmi Koyejo2023 · 342 citations
6. Scaling Laws for Autoregressive Generative ModelingarXiv:2010.14701Tom Henighan, Jared Kaplan, Mor Katz, et al.2020 · 277 citations
7. 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
8. Beyond neural scaling laws: beating power law scaling via data pruningConference on Neural Information Processing Systems (NeurIPS)Ben Sorscher, Robert Geirhos, Shashank Shekhar, et al.2022 · 231 citations
9. Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language ModelsConference on Neural Information Processing Systems (NeurIPS)Kushal Tirumala, Aram H. Markosyan, Luke Zettlemoyer, et al.2022 · 190 citations
10. The case for 4-bit precision: k-bit Inference Scaling LawsarXiv:2212.09720Tim Dettmers, Luke Zettlemoyer2022 · 123 citations
11. Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language ModelsarXiv:2408.00724Yangzhen Wu, Zhiqing Sun, Shanda Li, et al.2024 · 122 citations
12. SemDeDup: Data-efficient learning at web-scale through semantic deduplicationarXiv:2303.09540Amro Abbas, Kushal Tirumala, Dániel Simig, et al.2023 · 108 citations
13. Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling LawsarXiv:2401.00448Nikhil Sardana, Jacob Portes, Sasha Doubov, et al.2024 · 57 citations
14. Language models scale reliably with over-training and on downstream tasksarXiv:2403.08540Samir Yitzhak Gadre, Georgios Smyrnis, Vaishaal Shankar, et al.2024 · 46 citations
15. Distillation Scaling LawsarXiv:2502.08606Dan Busbridge, Amitis Shidani, Floris Weers, et al.2025 · 26 citations
16. Pretraining on the Test Set Is All You NeedarXiv:2309.08632Rylan Schaeffer2023 · 24 citations
17. Resolving Discrepancies in Compute-Optimal Scaling of Language ModelsConference on Neural Information Processing Systems (NeurIPS)Tomer Porian, Mitchell Wortsman, Jenia Jitsev, et al.2024 · 19 citations

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

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