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Who cites «Training Compute-Optimal Large Language Models»

1,110 papers of our library cite it (of the 140,884 whose bibliographies we read) · 150 with a page here, the most cited first · page 2 of 2 · 101–150 · ← the paper

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  1. Moshi: a speech-text foundation model for real-time dialogueAlexandre Defossez, Laurent Mazare, Manu Orsini, et al.2024 · 154 citations
  2. TheoremQA: A Theorem-driven Question Answering DatasetWenhu Chen, Ming Yin, Max Ku, et al.2023 · 154 citations
  3. Leveraging Large Language Models for Multiple Choice Question AnsweringJoshua Robinson, Christopher Michael Rytting, David Wingate2022 · 154 citations
  4. Locally Typical SamplingClara Meister, Tiago Pimentel, Gian Wiher, et al.2023 · 152 citations
  5. SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-trainingTianzhe Chu, Yuexiang Zhai, Jihan Yang, et al.2025 · 151 citations
  6. AnnoLLM: Making Large Language Models to Be Better Crowdsourced AnnotatorsXingwei He, Zhenghao Lin, Yeyun Gong, et al.2023 · 150 citations
  7. How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data CompositionGuanting Dong, Hongyi Yuan, Keming Lu, et al.2024 · 145 citations
  8. The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual DatasetHugo Laurençon, Lucile Saulnier, Thomas Wang, et al.2023 · 145 citations
  9. Is GPT-3 a Good Data Annotator?Bosheng Ding, Chengwei Qin, Linlin Liu, et al.2022 · 144 citations
  10. Symbolic Chain-of-Thought Distillation: Small Models Can Also “Think” Step-by-StepLiunian Harold Li, Jack Hessel, Youngjae Yu, et al.2023 · 139 citations
  11. DeepInception: Hypnotize Large Language Model to Be JailbreakerXuan Li, Zhanke Zhou, Jianing Zhu, et al.2023 · 138 citations
  12. Efficient Training of Language Models to Fill in the MiddleMohammad Bavarian, Heewoo Jun, Nikolas Tezak, et al.2022 · 137 citations
  13. Understanding the Effects of RLHF on LLM Generalisation and DiversityRobert Kirk, Ishita Mediratta, Christoforos Nalmpantis, et al.2023 · 131 citations
  14. Improving Language Model Negotiation with Self-Play and In-Context Learning from AI FeedbackYao Fu, Hao Peng, Tushar Khot, et al.2023 · 129 citations
  15. Deductive Verification of Chain-of-Thought ReasoningZhan Ling, Yunhao Fang, Xuanlin Li, et al.2023 · 128 citations
  16. Large Language Model Alignment: A SurveyTianhao Shen, Renren Jin, Yufei Huang, et al.2023 · 123 citations
  17. 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
  18. ALLaM: Large Language Models for Arabic and EnglishM Saiful Bari, Yazeed Alnumay, Norah A. Alzahrani, et al.2024 · 113 citations
  19. Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-VerificationAojun Zhou, Ke Wang, Zimu Lu, et al.2023 · 109 citations
  20. Data Engineering for Scaling Language Models to 128K ContextYao Fu, Rameswar Panda, Xinyao Niu, et al.2024 · 107 citations
  21. Tokenizer Choice For LLM Training: Negligible or Crucial?Mehdi Ali, Michael Fromm, Klaudia Thellmann, et al.2024 · 102 citations
  22. Inverse Scaling: When Bigger Isn't BetterIan R. McKenzie, Alexander Lyzhov, Michael Pieler, et al.2023 · 99 citations
  23. SemDeDup: Data-efficient learning at web-scale through semantic deduplicationAmro Abbas, Kushal Tirumala, Dániel Simig, et al.2023 · 94 citations
  24. Skywork: A More Open Bilingual Foundation ModelTianwen Wei, Liang Zhao, Lichang Zhang, et al.2023 · 91 citations
  25. Language Modeling Is CompressionGregoire Deletang, Anian Ruoss, Paul-Ambroise Duquenne, et al.2023 · 89 citations
  26. D4: Improving LLM Pretraining via Document De-Duplication and DiversificationKushal Tirumala, Daniel Simig, Armen Aghajanyan, et al.2023 · 79 citations
  27. Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?Yi Tay, Mostafa Dehghani, Samira Abnar, et al.2023 · 79 citations
  28. Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in ChinchillaTom Lieberum, Matthew Rahtz, Janos Kramar, et al.2023 · 51 citations
  29. Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling LawsNikhil Sardana, Jacob Portes, Sasha Doubov, et al.2024 · 47 citations
  30. Scaling Laws with Vocabulary: Larger Models Deserve Larger VocabulariesChaofan Tao, Qian Liu, Longxu Dou, et al.2024 · 42 citations
  31. Transcending Scaling Laws with 0.1% Extra ComputeYi Tay, Jason Wei, Hyung Won Chung, et al.2022 · 40 citations
  32. Scaling Laws for Generative Mixed-Modal Language ModelsArmen Aghajanyan, Lili Yu, Alexis Conneau, et al.2023 · 39 citations
  33. Observational Scaling Laws and the Predictability of Language Model PerformanceYangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto2024 · 28 citations
  34. Taqyim: Evaluating Arabic NLP Tasks Using ChatGPT ModelsZaid Alyafeai, Maged S. Alshaibani, Badr AlKhamissi, et al.2023 · 25 citations
  35. Scaling Laws and Compute-Optimal Training Beyond Fixed Training DurationsAlexander Hägele, Elie Bakouch, Atli Kosson, et al.2024 · 24 citations
  36. Distillation Scaling LawsDan Busbridge, Amitis Shidani, Floris Weers, et al.2025 · 23 citations
  37. Jais 2: A Family of Arabic-Centric Open Large Language ModelsMohamed Anwar, Abed Alhakim Freihat, George Ibrahim, et al.2026 · 14 citations
  38. TigerCoder: A Novel Suite of LLMs for Code Generation in BanglaNishat Raihan, Antonios Anastasopoulos, Marcos Zampieri2026 · 13 citations
  39. When To Solve, When To Verify: Compute-Optimal Problem Solving and Generative Verification for LLM ReasoningNishad Singhi, Hritik Bansal, Arian Hosseini, et al.2025 · 12 citations
  40. Chinchilla Scaling: A replication attemptTamay Besiroglu, Ege Erdil, Matthew Barnett, et al.2024 · 12 citations
  41. Multilingual Sequence-to-Sequence Models for Hebrew NLPMatan Eyal, Hila Noga, Roee Aharoni, et al.2023 · 9 citations
  42. Revisiting Pre-trained Language Models and their Evaluation for Arabic Natural Language UnderstandingAbbas Ghaddar, Yimeng Wu, Sunyam Bagga, et al.2022 · 9 citations
  43. FAME: Fictional Actors for Multilingual ErasureClaudio Savelli, Moreno La Quatra, Alkis Koudounas, et al.2026 · 7 citations
  44. Towards more Human-like Language Models based on Contextualizer Pretraining StrategyChenghao Xiao, G Thomas Hudson, Noura Al Moubayed2023 · 7 citations
  45. Scaling Laws for Code: A More Data-Hungry RegimeXianzhen Luo, Wenzhen Zheng, Qingfu Zhu, et al.2026 · 3 citations
  46. When Flores Bloomz Wrong: Cross-Direction Contamination in Machine Translation EvaluationDavid Tan, Pinzhen Chen, Josef van Genabith, et al.2026 · 2 citations
  47. A Survey on Parameter-efficient Fine-tuning of Large Models: Techniques, Trends, and Challenges (大模型参数高效微调方法综述: 技术、趋势与挑战)Anda Tang (唐岸达), Zhouchen Lin (林宙辰)2026
  48. Sentiment Analysis and Language Models for KwanyamaNdapa Nakashole2026
  49. On the importance of Data Scale in Pretraining Arabic Language ModelsAbbas Ghaddar, Philippe Langlais, Mehdi Rezagholizadeh, et al.2024
  50. Questions After ChatGPT: Opportunities and Challenges of Very Large Pre-trained Models (问答ChatGPT之后: 超大预训练模型的机遇和挑战)Jingwei Lu (卢经纬), Chao Guo (郭超), Xingyuan Dai (戴星原), et al.2023

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