🔗 Who cites «Will we run out of data? Limits of LLM scaling based on human-generated data»

119 papers of our library cite it (of the 149,439 whose bibliographies we read) · 28 with a page here, the most cited first · ← back to «Will we run out of data? Limits of LLM scaling based on human-generated data»

1. A Survey of Large Language ModelsFrontiers of Computer ScienceWayne Xin Zhao, Kun Zhou, Junyi Li, et al.2023 · 2,875 citations
2. The Falcon Series of Open Language ModelsarXiv:2311.16867Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, et al.2023 · 443 citations
3. Kimi k1.5: Scaling Reinforcement Learning with LLMsarXiv:2501.12599Kimi Team, Angang Du, Bofei Gao, et al.2025 · 431 citations
4. Challenges and Applications of Large Language ModelsarXiv:2307.10169Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.2023 · 301 citations
5. Scaling Synthetic Data Creation with 1,000,000,000 PersonasarXiv:2406.20094Tao Ge, Xin Chan, Xiaoyang Wang, et al.2024 · 171 citations
6. Pretraining Language Models with Human PreferencesarXiv:2302.08582Tomasz Korbak, Kejian Shi, Angelica Chen, et al.2023 · 121 citations
7. Best Practices and Lessons Learned on Synthetic DataarXiv:2404.07503Ruibo Liu, Jerry Wei, Fangyu Liu, et al.2024 · 119 citations
8. A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & ToxicityarXiv:2305.13169Shayne Longpre, Gregory Yauney, Emily Reif, et al.2023 · 104 citations
9. Rephrasing the Web: A Recipe for Compute and Data-Efficient Language ModelingAnnual Meeting of the Association for Computational Linguistics (ACL)Pratyush Maini, Skyler Seto, Richard Bai, et al.2024 · 90 citations
10. Scaling Laws with Vocabulary: Larger Models Deserve Larger VocabulariesarXiv:2407.13623Chaofan Tao, Qian Liu, Longxu Dou, et al.2024 · 46 citations
11. 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
12. Dating ancient manuscripts using radiocarbon and AI-based writing style analysisMladen Popovic, Maruf A. Dhali, Lambert Schomaker, et al.2024 · 23 citations
13. What is the Role of Small Models in the LLM Era: A SurveyComputational LinguisticsLihu Chen, Gael Varoquaux2024 · 23 citations
14. A Survey of Post-Training Scaling in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Hanyu Lai, Xiao Liu, Junjie Gao, et al.2025 · 18 citations
15. MiniPLM: Knowledge Distillation for Pre-Training Language ModelsarXiv:2410.17215Yuxian Gu, Hao Zhou, Fandong Meng, et al.2024 · 12 citations
16. Between Circuits and Chomsky: Pre-pretraining on Formal Languages Imparts Linguistic BiasesAnnual Meeting of the Association for Computational Linguistics (ACL)Michael Y. Hu, Jackson Petty, Chuan Shi, et al.2025 · 9 citations
17. Detecting Training Data of Large Language Models via Expectation MaximizationConference of the European Chapter of the Association for Computational Linguistics (EACL)Gyuwan Kim, Yang Li, Evangelia Spiliopoulou, et al.2026 · 5 citations
18. Recitation over Reasoning: How Cutting-Edge Language Models Can Fail on Elementary School-Level Reasoning Problems?International Joint Conference on Natural Language Processing (IJCNLP)Kai Yan, Yufei Xu, Zhengyin Du, et al.2025 · 5 citations
19. Guided by Gut: Efficient Test-Time Scaling with Reinforced Intrinsic ConfidenceAnnual Meeting of the Association for Computational Linguistics (ACL)Amirhosein Ghasemabadi, Keith G. Mills, Baochun Li, et al.2026 · 4 citations
20. MathAgent: Adversarial Evolution of Constraint Graphs for Mathematical Reasoning Data SynthesisFindings of the Association for Computational LinguisticsZixiong Yu, Jun Rao, Guhan Chen, et al.2026 · 3 citations
21. ARISE: An Adaptive Resolution-Aware Metric for Test-Time Scaling Evaluation in Large Reasoning ModelsFindings of the Association for Computational LinguisticsZhangyue Yin, Qiushi Sun, Zhiyuan Zeng, et al.2026 · 1 citation
22. On Safety Risks in Experience-Driven Self-Evolving AgentsFindings of the Association for Computational LinguisticsWeixiang Zhao, Yichen Zhang, Yingshuo Wang, et al.2026 · 1 citation
23. CoTEvol: Self-Evolving Chain-of-Thoughts for Data Synthesis in Mathematical ReasoningFindings of the Association for Computational LinguisticsZhuo Wang, Zhuo Zhang, Yafu Li, et al.2026
24. Legal Considerations in the Use of Synthetic Data for AI Development and Finetuning: The Case of LLMs4EUJoint Workshop on Legal and Ethical Issues in Human Language Technologies and Computational Approaches to Language Data Pseudonymization, Anonymization, De-identification, and Data Privacy @ LRECKossay Talmoudi, Khalid Choukri, Amélie Gourgeot, et al.2026
25. LinkQA: Synthesizing Diverse QA from Multiple Seeds Strongly Linked by Knowledge PointsAnnual Meeting of the Association for Computational Linguistics (ACL)Xuemiao Zhang, Can Ren, Chengying Tu, et al.2026
26. Synthetic Eggs in Many Baskets: The Impact of Synthetic Data Diversity on LLM Fine-TuningFindings of the Association for Computational LinguisticsMax Schaffelder, Albert Gatt2026
27. UniGeM: Unifying Data Selection and Mixing via Geometric Exploration and MiningFindings of the Association for Computational LinguisticsChanghao Wang, Yunfeiyu, Xinhao Yao, et al.2026
28. Survey on Hallucinations in Large Language Models (大语言模型的幻觉问题研究综述)Journal of SoftwareZeyuan Liu (刘泽垣), Pengjiang Wang (王鹏江), Xiaobin Song (宋晓斌), et al.2024

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