--- title: "Who cites Synthetic Data (Almost) from — 59 citing papers" description: "59 papers of the Elamur Research library cite «Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models»…" canonical: "https://research.elamur.ai/free/en/papers/li-2024-synthetic-data-almost-scratch-generalized/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/li-2024-synthetic-data-almost-scratch-generalized/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Synthetic Data (Almost) from Scratch: Generalized…](https://md.research.elamur.ai/free/en/papers/li-2024-synthetic-data-almost-scratch-generalized/) / cited by # Who cites «Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models» 59 papers of our library cite it (of the 149,439 whose bibliographies we read) · 13 with a page here, the most cited first · [← back to «Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models»](https://md.research.elamur.ai/free/en/papers/li-2024-synthetic-data-almost-scratch-generalized/) 1. [**Scaling Synthetic Data Creation with 1,000,000,000 Personas arXiv:2406.20094**Tao Ge, Xin Chan, Xiaoyang Wang, et al.*2024 · 171 citations*](https://md.research.elamur.ai/free/en/papers/ge-2024-scaling-synthetic-data-creation-1/) 2. [**Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing arXiv:2406.08464**Zhangchen Xu, Fengqing Jiang, Luyao Niu, et al.*2024 · 148 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-magpie-alignment-data-synthesis-scratch/) 3. **[Instruction Pre-Training: Language Models are Supervised Multitask Learners](https://md.research.elamur.ai/free/en/papers/cheng-2024-instruction-pre-training-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Daixuan Cheng, Yuxian Gu, Shaohan Huang, et al.*2024 · 59 citations* 4. [**On the Diversity of Synthetic Data and its Impact on Training Large Language Models arXiv:2410.15226**Hao Chen, Abdul Waheed, Xiang Li, et al.*2024 · 35 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-diversity-synthetic-data-impact-training/) 5. [**Multilingual Arbitrage: Optimizing Data Pools to Accelerate Multilingual Progress arXiv:2408.14960**Ayomide Odumakinde, Daniel D'souza, Pat Verga, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/odumakinde-2024-multilingual-arbitrage-optimizing-data-pools/) 6. **[Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion](https://md.research.elamur.ai/free/en/papers/zhu-2025-second-language-arabic-acquisition-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jianqing Zhu, Huang Huang, Zhihang Lin, et al.*2025 · 11 citations* 7. [**Boosting Reward Model with Preference-Conditional Multi-Aspect Synthetic Data Generation arXiv:2407.16008**Jiaming Shen, Ran Xu, Yennie Jun, et al.*2024 · 5 citations*](https://md.research.elamur.ai/free/en/papers/shen-2024-boosting-reward-model-preference-conditional/) 8. **[A Survey on LLM-based Conversational User Simulation](https://md.research.elamur.ai/free/en/papers/ni-2026-survey-llm-based-conversational-user/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Bo Ni, Yu Wang, Leyao Wang, et al.*2026 · 1 citation* 9. **[A Teacher-Student Approach to Creating Verified Synthetic Clarification and Correction Dialogues for TableQA Tasks](https://md.research.elamur.ai/free/en/papers/poelitz-2026-teacher-student-approach-creating-verified/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Christian Poelitz, Nick McKenna*2026* 10. **[Beyond Sample-Level Feedback: Using Reference-Level Feedback to Guide Data Synthesis](https://md.research.elamur.ai/free/en/papers/mehri-2026-beyond-sample-level-feedback-reference/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Shuhaib Mehri, Xiusi Chen, Heng Ji, et al.*2026* 11. **[PersonaTrace: Synthesizing Realistic Digital Footprints with LLM Agents](https://md.research.elamur.ai/free/en/papers/wang-2026-personatrace-synthesizing-realistic-digital-footprints/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Minjia Wang, Yunfeng Wang, Xiao Ma, et al.*2026* 12. **[RV-Syn: Rational and Verifiable Mathematical Reasoning Data Synthesis based on Structured Function Library](https://md.research.elamur.ai/free/en/papers/wang-2026-rv-syn-rational-verifiable-mathematical/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiapeng Wang, Jinhao Jiang, Zhiqiang Zhang, et al.*2026* 13. **[Synthetic Instruction Generation for Low-Resource Nordic Languages: Viability and Limitations in LLM Instruction-Tuning](https://md.research.elamur.ai/free/en/papers/stenlund-2026-synthetic-instruction-generation-low-resource/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Mathias Stenlund, Annika Simonsen, Lars Bungum, 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/li-2024-synthetic-data-almost-scratch-generalized/) — Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models