🔗 Who cites «RegMix: Data Mixture as Regression for Language Model Pre-training»

54 papers of our library cite it (of the 149,439 whose bibliographies we read) · 14 with a page here, the most cited first · ← back to «RegMix: Data Mixture as Regression for Language Model Pre-training»

1. Qwen3 Technical ReportarXiv:2505.09388An Yang, Anfeng Li, Baosong Yang, et al.2025 · 3,382 citations
2. Olmo 3arXiv:2512.13961Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.2025 · 130 citations
3. A Comprehensive Survey on Long Context Language ModelingarXiv:2503.17407Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.2025 · 64 citations
4. olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language ModelsarXiv:2502.18443Jake Poznanski, Aman Rangapur, Jon Borchardt, et al.2025 · 44 citations
5. Hunyuan-MT Technical ReportarXiv:2509.05209Mao Zheng, Zheng Li, Bingxin Qu, et al.2025 · 17 citations
6. Scaling Laws for Code: A More Data-Hungry RegimeAnnual Meeting of the Association for Computational Linguistics (ACL)Xianzhen Luo, Wenzhen Zheng, Qingfu Zhu, et al.2026 · 3 citations
7. Data Mixing Agent: Learning to Re-weight Domains for Continual Pre-trainingAnnual Meeting of the Association for Computational Linguistics (ACL)Kailai Yang, Xiao Liu, Lei Ji, et al.2026 · 1 citation
8. SCAN: Structured Capability Assessment and Navigation for LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Zongqi Wang, Tianle Gu, Chen Gong, et al.2026 · 1 citation
9. Alignment Tuning for Large Language Models: A Data-Centric Lens on Alignment Data PipelinesFindings of the Association for Computational LinguisticsHwanjun Song2026
10. 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
11. Data Foundations of Long-Context Language Models: A SurveyTransactions of the Association for Computational Linguistics (TACL)Zechen Sun, Yuyang Sun, Zhaochen Su, et al.2026
12. DynamixSFT: Dynamic Mixture Optimization of Instruction Tuning CollectionsFindings of the Association for Computational LinguisticsHaebin Shin, Lei Ji, Xiao Liu, et al.2026
13. 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
14. 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

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

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