🔗 Who cites «How to Train Data-Efficient LLMs»

45 papers of our library cite it (of the 149,439 whose bibliographies we read) · 12 with a page here, the most cited first · ← back to «How to Train Data-Efficient LLMs»

1. A Survey of Large Language ModelsFrontiers of Computer ScienceWayne Xin Zhao, Kun Zhou, Junyi Li, et al.2023 · 2,875 citations
2. InternLM2 Technical ReportarXiv:2403.17297Zheng Cai, Maosong Cao, Haojiong Chen, et al.2024 · 301 citations
3. 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
4. RegMix: Data Mixture as Regression for Language Model Pre-trainingarXiv:2407.01492Qian Liu, Xiaosen Zheng, Niklas Muennighoff, et al.2024 · 57 citations
5. Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem SolvingarXiv:2405.12205Aniket Didolkar, Anirudh Goyal, Nan Rosemary Ke, et al.2024 · 45 citations
6. On the Diversity of Synthetic Data and its Impact on Training Large Language ModelsarXiv:2410.15226Hao Chen, Abdul Waheed, Xiang Li, et al.2024 · 35 citations
7. From Selection to Generation: A Survey of LLM-based Active LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie, et al.2025 · 22 citations
8. MDCure: A Scalable Pipeline for Multi-Document Instruction-FollowingAnnual Meeting of the Association for Computational Linguistics (ACL)Gabrielle Kaili-May Liu, Bowen Shi, Avi Caciularu, et al.2025 · 11 citations
9. Beyond Random Sampling: Efficient Language Model Pretraining via Curriculum LearningConference of the European Chapter of the Association for Computational Linguistics (EACL)Yang Zhang, Amr Mohamed, Hadi Abdine, et al.2026 · 7 citations
10. DOSE: Data Selection for Multi-Modal LLMs via Off-the-Shelf ModelsFindings of the Association for Computational LinguisticsBiao Wu, Yiwu Zhong, Meng Fang, et al.2026
11. GALA: Geometric Data Selection with Strategic Prospecting for Large Language Model Self-trainingFindings of the Association for Computational LinguisticsZhongwei Xie, Ruihao Liao, Zimo Wang, et al.2026
12. 360Zhinao Technical ReportarXiv:2405.133862024

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

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