🔗 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»

1. The Llama 3 Herd of ModelsarXiv:2407.21783Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, et al.2024 · 7,682 citations
2. Qwen Technical ReportarXiv:2309.16609Jinze Bai, Shuai Bai, Yunfei Chu, et al.2023 · 1,669 citations
3. Yi: Open Foundation Models by 01.AIarXiv:2403.04652Alex Young, Bei Chen, Chao Li, et al.2024 · 408 citations
4. What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction TuningarXiv:2312.15685Wei Liu, Weihao Zeng, Keqing He, et al.2023 · 178 citations
5. How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data CompositionAnnual Meeting of the Association for Computational Linguistics (ACL)Guanting Dong, Hongyi Yuan, Keming Lu, et al.2024 · 145 citations
6. A Survey on Data Selection for Language ModelsarXiv:2402.16827Alon Albalak, Yanai Elazar, Sang Michael Xie, et al.2024 · 95 citations
7. Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-AlignmentAnnual Meeting of the Association for Computational Linguistics (ACL)Keming Lu, Bowen Yu, Chang Zhou, et al.2024 · 86 citations
8. HelpSteer2: Open-source dataset for training top-performing reward modelsarXiv:2406.08673Zhilin Wang, Yi Dong, Olivier Delalleau, et al.2024 · 82 citations
9. DART-Math: Difficulty-Aware Rejection Tuning for Mathematical Problem-SolvingarXiv:2407.13690Yuxuan Tong, Xiwen Zhang, Rui Wang, et al.2024 · 81 citations
10. HelpSteer: Multi-attribute Helpfulness Dataset for SteerLMConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Zhilin Wang, Yi Dong, Jiaqi Zeng, et al.2024 · 72 citations
11. Routing to the Expert: Efficient Reward-guided Ensemble of Large Language ModelsarXiv:2311.08692Keming Lu, Hongyi Yuan, Runji Lin, et al.2023 · 65 citations
12. One-Shot Learning as Instruction Data Prospector for Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Yunshui Li, Binyuan Hui, Xiaobo Xia, et al.2024 · 61 citations
13. Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-TuningarXiv:2402.00530Ming Li, Yong Zhang, Shwai He, et al.2024 · 60 citations
14. Automatic Instruction Evolving for Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Weihao Zeng, Can Xu, Yingxiu Zhao, et al.2024 · 28 citations
15. Seed-Coder: Let the Code Model Curate Data for ItselfarXiv:2506.03524ByteDance Seed, Yuyu Zhang, Jing Su, et al.2025 · 25 citations
16. A Survey on Efficient Large Language Model Training: From Data-centric PerspectivesAnnual Meeting of the Association for Computational Linguistics (ACL)Junyu Luo, Bohan Wu, Xiao Luo, et al.2025 · 14 citations
17. RAG-Critic: Leveraging Automated Critic-Guided Agentic Workflow for Retrieval Augmented GenerationAnnual Meeting of the Association for Computational Linguistics (ACL)Guanting Dong, Jiajie Jin, Xiaoxi Li, et al.2025 · 14 citations
18. Progressive Multimodal Reasoning via Active RetrievalAnnual Meeting of the Association for Computational Linguistics (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 ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Chao Xue, Yao Wang, Mengqiao Liu, et al.2026 · 6 citations
20. Baichuan Alignment Technical ReportarXiv:2410.14940Mingan Lin, Fan Yang, Yanjun Shen, et al.2024 · 5 citations
21. Select2Reason: Efficient Instruction-Tuning Data Selection for Long-CoT ReasoningFindings of the Association for Computational LinguisticsCehao Yang, Xueyuan Lin, Xiaojun Wu, et al.2026 · 4 citations
22. 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
23. T-pro 2.0: An Efficient Russian Hybrid-Reasoning Model and PlaygroundConference of the European Chapter of the Association for Computational Linguistics (EACL)Dmitrii Stoianov, Danil Taranets, Olga Tsymboi, et al.2026 · 2 citations
24. CypherSmith: Transforming Text-to-Cypher Generation for LLMs with Synthetic DataAnnual Meeting of the Association for Computational Linguistics (ACL)Zeyu Zhang, Kexuan Sun, Zheng Tang, et al.2026
25. InstructDiff: Domain-Adaptive Data Selection via Contrastive Entropy for Efficient LLM Fine-TuningAnnual Meeting of the Association for Computational Linguistics (ACL)Junyou Su, He Zhu, Xiao Luo, et al.2026
26. Safeguarding LLM Fine-tuning via Push-Pull Distributional AlignmentAnnual Meeting of the Association for Computational Linguistics (ACL)Haozhong Wang, Zhuo Li, Yibo Yang, et al.2026

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