--- title: "Who cites What Language Model to Train if — 67 citing papers" description: "67 papers of the Elamur Research library cite «What Language Model to Train if You Have One Million GPU Hours?», found in their…" canonical: "https://research.elamur.ai/free/en/papers/scao-2022-language-model-train-if-have/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/scao-2022-language-model-train-if-have/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [What Language Model to Train if You Have One…](https://md.research.elamur.ai/free/en/papers/scao-2022-language-model-train-if-have/) / cited by # Who cites «What Language Model to Train if You Have One Million GPU Hours?» 67 papers of our library cite it (of the 149,439 whose bibliographies we read) · 23 with a page here, the most cited first · [← back to «What Language Model to Train if You Have One Million GPU Hours?»](https://md.research.elamur.ai/free/en/papers/scao-2022-language-model-train-if-have/) 1. **[A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,875 citations* 2. [**Crosslingual Generalization through Multitask Finetuning arXiv:2211.01786**Niklas Muennighoff, Thomas Wang, Lintang Sutawika, et al.*2022 · 703 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-crosslingual-generalization-through-multitask-finetuning/) 3. [**BloombergGPT: A Large Language Model for Finance arXiv:2303.17564**Shijie Wu, Ozan Irsoy, Steven Lu, et al.*2023 · 618 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-bloomberggpt-large-language-model-finance/) 4. [**Few-shot Learning with Multilingual Language Models arXiv:2112.10668**Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, et al.*2021 · 500 citations*](https://md.research.elamur.ai/free/en/papers/lin-2021-few-shot-learning-multilingual-language/) 5. [**The Falcon Series of Open Language Models arXiv:2311.16867**Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, et al.*2023 · 443 citations*](https://md.research.elamur.ai/free/en/papers/almazrouei-2023-falcon-series-open-language-models/) 6. **[Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning](https://md.research.elamur.ai/free/en/papers/singh-2024-aya-dataset-open-access-collection/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shivalika Singh, Freddie Vargus, Daniel D'souza, et al.*2024 · 207 citations* 7. [**Will we run out of data? Limits of LLM scaling based on human-generated data arXiv:2211.04325**Pablo Villalobos, Anson Ho, Jaime Sevilla, et al.*2022 · 198 citations*](https://md.research.elamur.ai/free/en/papers/villalobos-2022-will-run-out-data-limits/) 8. **[DataComp-LM: In search of the next generation of training sets for language models](https://md.research.elamur.ai/free/en/papers/li-2024-datacomp-lm-search-next-generation/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jeffrey Li, Alex Fang, Georgios Smyrnis, et al.*2024 · 191 citations* 9. [**Generative Representational Instruction Tuning arXiv:2402.09906**Niklas Muennighoff, Hongjin Su, Liang Wang, et al.*2024 · 172 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2024-generative-representational-instruction-tuning/) 10. **[The Impact of Positional Encoding on Length Generalization in Transformers](https://md.research.elamur.ai/free/en/papers/kazemnejad-2023-impact-positional-encoding-length-generalization/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Amirhossein Kazemnejad, Inkit Padhi, Karthikeyan Natesan Ramamurthy, et al.*2023 · 134 citations* 11. [**EuroLLM: Multilingual Language Models for Europe arXiv:2409.16235**Pedro Henrique Martins, Patrick Fernandes, Joao Alves, et al.*2024 · 133 citations*](https://md.research.elamur.ai/free/en/papers/martins-2024-eurollm-multilingual-language-models-europe/) 12. **[Emergent and Predictable Memorization in Large Language Models](https://md.research.elamur.ai/free/en/papers/biderman-2023-emergent-predictable-memorization-large-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Stella Biderman, USVSN Sai Prashanth, Lintang Sutawika, et al.*2023 · 123 citations* 13. [**Transformer Language Models without Positional Encodings Still Learn Positional Information arXiv:2203.16634**Adi Haviv, Ori Ram, Ofir Press, et al.*2022 · 111 citations*](https://md.research.elamur.ai/free/en/papers/haviv-2022-transformer-language-models-positional-encodings/) 14. [**A Survey on Data Selection for Language Models arXiv:2402.16827**Alon Albalak, Yanai Elazar, Sang Michael Xie, et al.*2024 · 95 citations*](https://md.research.elamur.ai/free/en/papers/albalak-2024-survey-data-selection-language-models/) 15. [**Large Language Models in Law: A Survey arXiv:2312.03718**Jinqi Lai, Wensheng Gan, Jiayang Wu, et al.*2023 · 80 citations*](https://md.research.elamur.ai/free/en/papers/lai-2023-large-language-models-law-survey/) 16. **[FOCUS: Effective Embedding Initialization for Monolingual Specialization of Multilingual Models](https://md.research.elamur.ai/free/en/papers/dobler-2023-focus-effective-embedding-initialization-monolingual/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Konstantin Dobler, Gerard de Melo*2023 · 62 citations* 17. [**Language models scale reliably with over-training and on downstream tasks arXiv:2403.08540**Samir Yitzhak Gadre, Georgios Smyrnis, Vaishaal Shankar, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/gadre-2024-language-models-scale-reliably-over/) 18. [**Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies arXiv:2407.13623**Chaofan Tao, Qian Liu, Longxu Dou, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/tao-2024-scaling-laws-vocabulary-larger-models/) 19. [**EuroLLM-9B: Technical Report arXiv:2506.04079**Pedro Henrique Martins, Joao Alves, Patrick Fernandes, et al.*2025 · 45 citations*](https://md.research.elamur.ai/free/en/papers/martins-2025-eurollm-9b-technical-report/) 20. [**Efficient Language Model Training through Cross-Lingual and Progressive Transfer Learning arXiv:2301.09626**Malte Ostendorff, Georg Rehm*2023 · 36 citations*](https://md.research.elamur.ai/free/en/papers/ostendorff-2023-efficient-language-model-training-through/) 21. [**TransNormerLLM: A Faster and Better Large Language Model with Improved TransNormer arXiv:2307.14995**Zhen Qin, Dong Li, Weigao Sun, et al.*2023 · 33 citations*](https://md.research.elamur.ai/free/en/papers/qin-2023-transnormerllm-faster-better-large/) 22. **[Merge and Conquer: Instructing Multilingual Models by Adding Target Language Weights](https://md.research.elamur.ai/free/en/papers/valero-2026-merge-conquer-instructing-multilingual-models/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Eneko Valero, Maria Ribalta i Albado, Oscar Sainz, et al.*2026* 23. [**360Zhinao Technical Report arXiv:2405.13386***2024*](https://md.research.elamur.ai/free/en/papers/360zhinao-team-2024-360zhinao-technical-report/) 44 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/scao-2022-language-model-train-if-have/) — What Language Model to Train if You Have One Million GPU Hours?