--- title: "Who cites QuRating: Selecting High-Quality — 68 citing papers" description: "68 papers of the Elamur Research library cite «QuRating: Selecting High-Quality Data for Training Language Models», found in their…" canonical: "https://research.elamur.ai/free/en/papers/wettig-2024-qurating-selecting-high-quality-data/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/wettig-2024-qurating-selecting-high-quality-data/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [QuRating: Selecting High-Quality Data for…](https://md.research.elamur.ai/free/en/papers/wettig-2024-qurating-selecting-high-quality-data/) / cited by # Who cites «QuRating: Selecting High-Quality Data for Training Language Models» 68 papers of our library cite it (of the 149,439 whose bibliographies we read) · 22 with a page here, the most cited first · [← back to «QuRating: Selecting High-Quality Data for Training Language Models»](https://md.research.elamur.ai/free/en/papers/wettig-2024-qurating-selecting-high-quality-data/) 1. **[A Survey on Large Language Models for Code Generation](https://md.research.elamur.ai/free/en/papers/jiang-2024-survey-large-language-models-code/) [ACM Transactions on Software Engineering and Methodology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-software-engineering-and-methodology/)**Juyong Jiang, Fan Wang, Jiasi Shen, et al.*2024 · 367 citations* 2. [**InternLM2 Technical Report arXiv:2403.17297**Zheng Cai, Maosong Cao, Haojiong Chen, et al.*2024 · 301 citations*](https://md.research.elamur.ai/free/en/papers/cai-2024-internlm2-technical-report/) 3. **[DataComp-LM: In search of the next generation of training sets for language models](https://md.research.elamur.ai/free/en/papers/li-2024-datacomp-lm-search-next-generation/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jeffrey Li, Alex Fang, Georgios Smyrnis, et al.*2024 · 191 citations* 4. **[A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness](https://md.research.elamur.ai/free/en/papers/wang-2024-comprehensive-survey-small-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Fali Wang, Zhiwei Zhang, Xianren Zhang, et al.*2024 · 159 citations* 5. [**A Survey on Data Selection for Language Models arXiv:2402.16827**Alon Albalak, Yanai Elazar, Sang Michael Xie, et al.*2024 · 95 citations*](https://md.research.elamur.ai/free/en/papers/albalak-2024-survey-data-selection-language-models/) 6. **[How to Train Long-Context Language Models (Effectively)](https://md.research.elamur.ai/free/en/papers/gao-2025-how-train-long-context-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianyu Gao, Alexander Wettig, Howard Yen, et al.*2025 · 89 citations* 7. [**A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well? arXiv:2503.24235**Qiyuan Zhang, Fuyuan Lyu, Zexu Sun, et al.*2025 · 79 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-survey-test-time-scaling-large/) 8. [**RegMix: Data Mixture as Regression for Language Model Pre-training arXiv:2407.01492**Qian Liu, Xiaosen Zheng, Niklas Muennighoff, et al.*2024 · 57 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-regmix-data-mixture-regression-language/) 9. **[What is the Role of Small Models in the LLM Era: A Survey](https://md.research.elamur.ai/free/en/papers/chen-2024-role-small-models-llm-era/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Lihu Chen, Gael Varoquaux*2024 · 23 citations* 10. [**Unearthing Large Scale Domain-Specific Knowledge from Public Corpora arXiv:2401.14624**Zhaoye Fei, Yunfan Shao, Linyang Li, et al.*2024 · 10 citations*](https://md.research.elamur.ai/free/en/papers/fei-2024-unearthing-large-scale-domain-specific/) 11. **[Add-One-In: Incremental Sample Selection for Large Language Models via a Choice-Based Greedy Paradigm](https://md.research.elamur.ai/free/en/papers/li-2025-add-one-incremental-sample-selection/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhuo Li, Yuhao Du, Xiaoqi Jiao, et al.*2025 · 5 citations* 12. **[Long-Chain Reasoning Distillation via Adaptive Prefix Alignment](https://md.research.elamur.ai/free/en/papers/liu-2026-long-chain-reasoning-distillation-adaptive/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhenghao Liu, Zhuoyang Wu, Xinze Li, et al.*2026 · 3 citations* 13. **[SynthRL: Scaling Visual Reasoning with Verifiable Data Synthesis](https://md.research.elamur.ai/free/en/papers/wu-2026-synthrl-scaling-visual-reasoning-verifiable/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zijian Wu, Jinjie Ni, Xiangyan Liu, et al.*2026 · 3 citations* 14. **[Enhancing Long-Chain Reasoning Distillation through Error-Aware Self-Reflection](https://md.research.elamur.ai/free/en/papers/wu-2026-enhancing-long-chain-reasoning-distillation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhuoyang Wu, Xinze Li, Zhenghao Liu, et al.*2026 · 1 citation* 15. **[From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan](https://md.research.elamur.ai/free/en/papers/yang-2026-curated-data-scalable-models-continual/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lei Yang, Leiyu Pan, Bojian Xiong, et al.*2026 · 1 citation* 16. **[Data-Efficient Adaptation to Contextual Shifts in LLM-based Conversational Recommendation](https://md.research.elamur.ai/free/en/papers/yang-2026-data-efficient-adaptation-contextual-shifts/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hyeongjun Yang, Donghyun Kim, Seokju Hwang, et al.*2026* 17. **[Detecting Non-Membership in LLM Training Data via Rank Correlations](https://md.research.elamur.ai/free/en/papers/shetty-2026-detecting-non-membership-llm-training/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Pranav Shetty, Mirazul Haque, Zhiqiang Ma, et al.*2026* 18. [**How Does "English (US)" Become the Default? Triangulating Structural Bias Towards American English Across the LLM Pipeline arXiv:2604.04204**Mir Tafseer Nayeem, Davood Rafiei*2026*](https://md.research.elamur.ai/free/en/papers/nayeem-2026-english-us-become-default-triangulating/) 19. **[InstructDiff: Domain-Adaptive Data Selection via Contrastive Entropy for Efficient LLM Fine-Tuning](https://md.research.elamur.ai/free/en/papers/su-2026-instructdiff-domain-adaptive-data-selection/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junyou Su, He Zhu, Xiao Luo, et al.*2026* 20. **[Large-Scale Diverse Synthesis for Mid-Training](https://md.research.elamur.ai/free/en/papers/zhang-2026-large-scale-diverse-synthesis-mid/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xuemiao Zhang, Chengying Tu, Can Ren, et al.*2026* 21. **[LinkQA: Synthesizing Diverse QA from Multiple Seeds Strongly Linked by Knowledge Points](https://md.research.elamur.ai/free/en/papers/zhang-2026-linkqa-synthesizing-diverse-qa-multiple/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xuemiao Zhang, Can Ren, Chengying Tu, et al.*2026* 22. **[Rethinking Data Mixing from the Perspective of Large Language Models](https://md.research.elamur.ai/free/en/papers/xu-2026-rethinking-data-mixing-perspective-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuanjian Xu, Tianze Sun, Changwei Xu, et al.*2026* 46 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/wettig-2024-qurating-selecting-high-quality-data/) — QuRating: Selecting High-Quality Data for Training Language Models