🔗 Who cites «Language models scale reliably with over-training and on downstream tasks»

42 papers of our library cite it (of the 149,439 whose bibliographies we read) · 12 with a page here, the most cited first · ← back to «Language models scale reliably with over-training and on downstream tasks»

1. DataComp-LM: In search of the next generation of training sets for language modelsConference on Neural Information Processing Systems (NeurIPS)Jeffrey Li, Alex Fang, Georgios Smyrnis, et al.2024 · 191 citations
2. Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling LawsarXiv:2401.00448Nikhil Sardana, Jacob Portes, Sasha Doubov, et al.2024 · 57 citations
3. RegMix: Data Mixture as Regression for Language Model Pre-trainingarXiv:2407.01492Qian Liu, Xiaosen Zheng, Niklas Muennighoff, et al.2024 · 57 citations
4. Scaling Laws with Vocabulary: Larger Models Deserve Larger VocabulariesarXiv:2407.13623Chaofan Tao, Qian Liu, Longxu Dou, et al.2024 · 46 citations
5. Paloma: A Benchmark for Evaluating Language Model FitConference on Neural Information Processing Systems (NeurIPS)Ian Magnusson, Akshita Bhagia, Valentin Hofmann, et al.2023 · 42 citations
6. Observational Scaling Laws and the Predictability of Language Model PerformancearXiv:2405.10938Yangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto2024 · 40 citations
7. Scaling Laws and Compute-Optimal Training Beyond Fixed Training DurationsConference on Neural Information Processing Systems (NeurIPS)Alexander Hägele, Elie Bakouch, Atli Kosson, et al.2024 · 31 citations
8. Distillation Scaling LawsarXiv:2502.08606Dan Busbridge, Amitis Shidani, Floris Weers, et al.2025 · 26 citations
9. 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
10. Bilingual Adaptation of Monolingual Foundation ModelsarXiv:2407.12869Gurpreet Gosal, Yishi Xu, Gokul Ramakrishnan, et al.2024 · 7 citations
11. Rethinking Data Mixture for Large Language Models: A Comprehensive Survey and New PerspectivesFindings of the Association for Computational LinguisticsYajiao Liu, Congliang Chen, Junchi Yang, et al.2026
12. UniGeM: Unifying Data Selection and Mixing via Geometric Exploration and MiningFindings of the Association for Computational LinguisticsChanghao Wang, Yunfeiyu, Xinhao Yao, et al.2026

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

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