🔗 Who cites «Deep Learning Scaling is Predictable, Empirically»

97 papers of our library cite it (of the 140,884 whose bibliographies we read) · 27 with a page here, the most cited first · ← the paper

1. Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, et al.2020 · 10,782 citations
2. Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, et al.2021 · 8,295 citations
3. Exploring the Limits of Transfer Learning with a Unified Text-to-Text TransformerColin Raffel, Noam Shazeer, Adam Roberts, et al.2019 · 7,433 citations
4. LLaMA: Open and Efficient Foundation Language ModelsHugo Touvron, Thibaut Lavril, Gautier Izacard, et al.2023 · 5,609 citations
5. On the Opportunities and Risks of Foundation ModelsRishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.2021 · 3,044 citations
6. Scaling Laws for Neural Language ModelsJared Kaplan, Sam McCandlish, Tom Henighan, et al.2020 · 1,814 citations
7. Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelsAarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.2022 · 1,695 citations
8. Reproducible scaling laws for contrastive language-image learningMehdi Cherti, Romain Beaumont, Ross Wightman, et al.2022 · 750 citations
9. Massively Multilingual Neural Machine Translation in the Wild: Findings and ChallengesNaveen Arivazhagan, Ankur Bapna, Orhan Firat, et al.2019 · 555 citations
10. GLaM: Efficient Scaling of Language Models with Mixture-of-ExpertsNan Du, Yanping Huang, Andrew M. Dai, et al.2021 · 474 citations
11. The Falcon Series of Open Language ModelsEbtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, et al.2023 · 436 citations
12. Are Emergent Abilities of Large Language Models a Mirage?Rylan Schaeffer, Brando Miranda, Sanmi Koyejo2023 · 323 citations
13. Challenges and Applications of Large Language ModelsJean Kaddour, Joshua Harris, Maximilian Mozes, et al.2023 · 300 citations
14. Large Language Monkeys: Scaling Inference Compute with Repeated SamplingBradley Brown, Jordan Juravsky, Ryan Ehrlich, et al.2024 · 241 citations
15. Beyond neural scaling laws: beating power law scaling via data pruningBen Sorscher, Robert Geirhos, Shashank Shekhar, et al.2022 · 220 citations
16. The case for 4-bit precision: k-bit Inference Scaling LawsTim Dettmers, Luke Zettlemoyer2022 · 119 citations
17. Scaling Laws for Autoregressive Generative ModelingTom Henighan, Jared Kaplan, Mor Katz, et al.2020 · 115 citations
18. Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language ModelsYangzhen Wu, Zhiqing Sun, Shanda Li, et al.2024 · 114 citations
19. Scaling Laws for TransferDanny Hernandez, Jared Kaplan, Tom Henighan, et al.2021 · 102 citations
20. SemDeDup: Data-efficient learning at web-scale through semantic deduplicationAmro Abbas, Kushal Tirumala, Dániel Simig, et al.2023 · 94 citations
21. Scaling Laws and Interpretability of Learning from Repeated DataDanny Hernandez, Tom Brown, Tom Conerly, et al.2022 · 62 citations
22. Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling LawsNikhil Sardana, Jacob Portes, Sasha Doubov, et al.2024 · 47 citations
23. Scaling Laws for Generative Mixed-Modal Language ModelsArmen Aghajanyan, Lili Yu, Alexis Conneau, et al.2023 · 39 citations
24. Observational Scaling Laws and the Predictability of Language Model PerformanceYangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto2024 · 28 citations
25. The Quantization Model of Neural ScalingEric J. Michaud, Ziming Liu, Uzay Girit, et al.2023 · 27 citations
26. Distillation Scaling LawsDan Busbridge, Amitis Shidani, Floris Weers, et al.2025 · 23 citations
27. Resolving Discrepancies in Compute-Optimal Scaling of Language ModelsTomer Porian, Mitchell Wortsman, Jenia Jitsev, et al.2024 · 14 citations

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

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