🔗 Who cites «DsDm: Model-Aware Dataset Selection with Datamodels»

29 papers of our library cite it (of the 149,439 whose bibliographies we read) · 10 with a page here, the most cited first · ← back to «DsDm: Model-Aware Dataset Selection with Datamodels»

1. LESS: Selecting Influential Data for Targeted Instruction TuningarXiv:2402.04333Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, et al.2024 · 178 citations
2. A Survey on Data Selection for Language ModelsarXiv:2402.16827Alon Albalak, Yanai Elazar, Sang Michael Xie, et al.2024 · 95 citations
3. Instruction Mining: Instruction Data Selection for Tuning Large Language ModelsarXiv:2307.06290Yihan Cao, Yanbin Kang, Chi Wang, et al.2023 · 74 citations
4. Rho-1: Not All Tokens Are What You NeedarXiv:2404.07965Zhenghao Lin, Zhibin Gou, Yeyun Gong, et al.2024 · 59 citations
5. RegMix: Data Mixture as Regression for Language Model Pre-trainingarXiv:2407.01492Qian Liu, Xiaosen Zheng, Niklas Muennighoff, et al.2024 · 57 citations
6. How to Train Data-Efficient LLMsarXiv:2402.09668Noveen Sachdeva, Benjamin Coleman, Wang-Cheng Kang, et al.2024 · 50 citations
7. Skywork-Math: Data Scaling Laws for Mathematical Reasoning in Large Language Models — The Story Goes OnarXiv:2407.08348Liang Zeng, Liangjun Zhong, Liang Zhao, et al.2024 · 14 citations
8. MiniPLM: Knowledge Distillation for Pre-Training Language ModelsarXiv:2410.17215Yuxian Gu, Hao Zhou, Fandong Meng, et al.2024 · 12 citations
9. Data Efficient RLVR via Off-Policy Influence GuidanceAnnual Meeting of the Association for Computational Linguistics (ACL)Erle Zhu, Dazhi Jiang, Yuan Wang, et al.2026
10. Perplexity-Aware Data Scaling Law: Perplexity Landscapes Predict Performance for Continual Pre-trainingAnnual Meeting of the Association for Computational Linguistics (ACL)Lei Liu, Hao Zhu, Xiaoyan Yang, et al.2026

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

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