🔗 Who cites «Efficient Online Data Mixing For Language Model Pre-Training»

34 papers of our library cite it (of the 149,439 whose bibliographies we read) · 7 with a page here, the most cited first · ← back to «Efficient Online Data Mixing For Language Model Pre-Training»

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. A Survey on Data Selection for Language ModelsarXiv:2402.16827Alon Albalak, Yanai Elazar, Sang Michael Xie, et al.2024 · 95 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. DaMo: Data Mixing Optimizer in Fine-tuning Multimodal LLMs for Mobile Phone AgentsFindings of the Association for Computational LinguisticsKai Shi, Jun Yang, Ni Yang, et al.2026
5. DynamixSFT: Dynamic Mixture Optimization of Instruction Tuning CollectionsFindings of the Association for Computational LinguisticsHaebin Shin, Lei Ji, Xiao Liu, et al.2026
6. 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
7. TiKMiX: Efficient Semi-Dynamic Data Mixture via Data Influence for LLM Pre-trainingAnnual Meeting of the Association for Computational Linguistics (ACL)Yifan Wang, Binbinliu, Fengze Liu, et al.2026

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

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