---
title: "Who cites #InsTag: Instruction Tagging for — 107 citing papers"
description: "107 papers of the Elamur Research library cite «#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language…"
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---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [#InsTag: Instruction Tagging for Analyzing…](https://md.research.elamur.ai/free/en/papers/lu-2023-instag-instruction-tagging-analyzing-supervised/) / cited by

# Who cites «#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models»

107 papers of our library cite it (of the 149,439 whose bibliographies we read) · 26 with a page here, the most cited first · [← back to «#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models»](https://md.research.elamur.ai/free/en/papers/lu-2023-instag-instruction-tagging-analyzing-supervised/)

1. [**The Llama 3 Herd of Models arXiv:2407.21783**Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, et al.*2024 · 7,682 citations*](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/)
2. [**Qwen Technical Report arXiv:2309.16609**Jinze Bai, Shuai Bai, Yunfei Chu, et al.*2023 · 1,669 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-technical-report/)
3. [**Yi: Open Foundation Models by 01.AI arXiv:2403.04652**Alex Young, Bei Chen, Chao Li, et al.*2024 · 408 citations*](https://md.research.elamur.ai/free/en/papers/ai-2024-yi-open-foundation-models-01/)
4. [**What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning arXiv:2312.15685**Wei Liu, Weihao Zeng, Keqing He, et al.*2023 · 178 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-makes-good-data-alignment-comprehensive/)
5. **[How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition](https://md.research.elamur.ai/free/en/papers/dong-2024-how-abilities-large-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guanting Dong, Hongyi Yuan, Keming Lu, et al.*2024 · 145 citations*
6. [**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/)
7. **[Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignment](https://md.research.elamur.ai/free/en/papers/lu-2024-large-language-models-are-superpositions/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Keming Lu, Bowen Yu, Chang Zhou, et al.*2024 · 86 citations*
8. [**HelpSteer2: Open-source dataset for training top-performing reward models arXiv:2406.08673**Zhilin Wang, Yi Dong, Olivier Delalleau, et al.*2024 · 82 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-helpsteer2-open-source-dataset-training/)
9. [**DART-Math: Difficulty-Aware Rejection Tuning for Mathematical Problem-Solving arXiv:2407.13690**Yuxuan Tong, Xiwen Zhang, Rui Wang, et al.*2024 · 81 citations*](https://md.research.elamur.ai/free/en/papers/tong-2024-dart-math-difficulty-aware-rejection/)
10. **[HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM](https://md.research.elamur.ai/free/en/papers/wang-2024-helpsteer-multi-attribute-helpfulness-dataset/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Zhilin Wang, Yi Dong, Jiaqi Zeng, et al.*2024 · 72 citations*
11. [**Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models arXiv:2311.08692**Keming Lu, Hongyi Yuan, Runji Lin, et al.*2023 · 66 citations*](https://md.research.elamur.ai/free/en/papers/lu-2023-routing-expert-efficient-reward-guided/)
12. **[One-Shot Learning as Instruction Data Prospector for Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2024-one-shot-learning-as-instruction/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yunshui Li, Binyuan Hui, Xiaobo Xia, et al.*2024 · 61 citations*
13. [**Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning arXiv:2402.00530**Ming Li, Yong Zhang, Shwai He, et al.*2024 · 60 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-superfiltering-weak-strong-data-filtering/)
14. **[Automatic Instruction Evolving for Large Language Models](https://md.research.elamur.ai/free/en/papers/zeng-2024-automatic-instruction-evolving-large-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Weihao Zeng, Can Xu, Yingxiu Zhao, et al.*2024 · 28 citations*
15. [**Seed-Coder: Let the Code Model Curate Data for Itself arXiv:2506.03524**ByteDance Seed, Yuyu Zhang, Jing Su, et al.*2025 · 25 citations*](https://md.research.elamur.ai/free/en/papers/bytedance-seed-2025-seed-coder-let-code/)
16. **[A Survey on Efficient Large Language Model Training: From Data-centric Perspectives](https://md.research.elamur.ai/free/en/papers/luo-2025-survey-efficient-large-language-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junyu Luo, Bohan Wu, Xiao Luo, et al.*2025 · 14 citations*
17. **[RAG-Critic: Leveraging Automated Critic-Guided Agentic Workflow for Retrieval Augmented Generation](https://md.research.elamur.ai/free/en/papers/dong-2025-rag-critic-leveraging-automated-critic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guanting Dong, Jiajie Jin, Xiaoxi Li, et al.*2025 · 14 citations*
18. **[Progressive Multimodal Reasoning via Active Retrieval](https://md.research.elamur.ai/free/en/papers/dong-2025-progressive-multimodal-reasoning-active-retrieval/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guanting Dong, Chenghao Zhang, Mengjie Deng, et al.*2025 · 10 citations*
19. **[Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models](https://md.research.elamur.ai/free/en/papers/xue-2026-why-supervised-fine-tuning-fails/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chao Xue, Yao Wang, Mengqiao Liu, et al.*2026 · 6 citations*
20. [**Baichuan Alignment Technical Report arXiv:2410.14940**Mingan Lin, Fan Yang, Yanjun Shen, et al.*2024 · 5 citations*](https://md.research.elamur.ai/free/en/papers/lin-2024-baichuan-alignment-technical-report/)
21. **[Select2Reason: Efficient Instruction-Tuning Data Selection for Long-CoT Reasoning](https://md.research.elamur.ai/free/en/papers/yang-2026-select2reason-efficient-instruction-tuning-data/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Cehao Yang, Xueyuan Lin, Xiaojun Wu, et al.*2026 · 4 citations*
22. **[MathAgent: Adversarial Evolution of Constraint Graphs for Mathematical Reasoning Data Synthesis](https://md.research.elamur.ai/free/en/papers/yu-2026-mathagent-adversarial-evolution-constraint-graphs/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zixiong Yu, Jun Rao, Guhan Chen, et al.*2026 · 3 citations*
23. **[T-pro 2.0: An Efficient Russian Hybrid-Reasoning Model and Playground](https://md.research.elamur.ai/free/en/papers/stoianov-2026-t-pro-2-0-efficient/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Dmitrii Stoianov, Danil Taranets, Olga Tsymboi, et al.*2026 · 2 citations*
24. **[CypherSmith: Transforming Text-to-Cypher Generation for LLMs with Synthetic Data](https://md.research.elamur.ai/free/en/papers/zhang-2026-cyphersmith-transforming-text-cypher-generation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zeyu Zhang, Kexuan Sun, Zheng Tang, et al.*2026*
25. **[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*
26. **[Safeguarding LLM Fine-tuning via Push-Pull Distributional Alignment](https://md.research.elamur.ai/free/en/papers/wang-2026-safeguarding-llm-fine-tuning-push/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haozhong Wang, Zhuo Li, Yibo Yang, et al.*2026*

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

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