--- title: "Who cites Shwai He — 66 papers of our library" description: "66 papers of the Elamur Research library cite the works of Shwai He, found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/authors/shwai-he/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/shwai-he/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Shwai He](https://md.research.elamur.ai/free/en/authors/shwai-he/) / cited by # Who cites Shwai He 66 citations of their works from 66 papers of our library (of the 149,439 whose bibliographies we read) · 18 with a page here, the most cited first · [← Shwai He](https://md.research.elamur.ai/free/en/authors/shwai-he/) 1. [**Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey arXiv:2403.14608**Zeyu Han, Chao Gao, Jinyang Liu, et al.*2024 · 240 citations*](https://md.research.elamur.ai/free/en/papers/han-2024-parameter-efficient-fine-tuning-large/) 2. [**From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning arXiv:2308.12032**Ming Li, Yong Zhang, Zhitao Li, et al.*2023 · 161 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-quantity-quality-boosting-llm-performance/) 3. [**PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models arXiv:2404.02948**Fanxu Meng, Zhaohui Wang, Muhan Zhang*2024 · 86 citations*](https://md.research.elamur.ai/free/en/papers/meng-2024-pissa-principal-singular-values-singular/) 4. [**Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities arXiv:2408.07666**Enneng Yang, Li Shen, Guibing Guo, et al.*2024 · 79 citations*](https://md.research.elamur.ai/free/en/papers/yang-2024-model-merging-llms-mllms-methods/) 5. [**OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs arXiv:2411.14199**Akari Asai, Jacqueline He, Rulin Shao, et al.*2024 · 62 citations*](https://md.research.elamur.ai/free/en/papers/asai-2024-openscholar-synthesizing-scientific-literature/) 6. [**SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models arXiv:2504.11468**Hardy Chen, Haoqin Tu, Fali Wang, et al.*2025 · 37 citations*](https://md.research.elamur.ai/free/en/papers/chen-2025-sft-rl-early-investigation-training/) 7. **[What Happened in LLMs Layers when Trained for Fast vs. Slow Thinking: A Gradient Perspective](https://md.research.elamur.ai/free/en/papers/li-2025-what-happened-llms-layers-when/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ming Li, Yanhong Li, Tianyi Zhou*2025 · 15 citations* 8. **[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* 9. **[Understanding the Thinking Process of Reasoning Models: A Perspective from Schoenfeld's Episode Theory](https://md.research.elamur.ai/free/en/papers/li-2025-understanding-thinking-process-reasoning-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ming Li, Nan Zhang, Chenrui Fan, et al.*2025 · 6 citations* 10. **[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* 11. **[ProFit: Leveraging High-Value Signals in SFT via Probability-Guided Token Selection](https://md.research.elamur.ai/free/en/papers/liu-2026-profit-leveraging-high-value-signals/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Tao Liu, Taiqiang Wu, Runming Yang, et al.*2026 · 4 citations* 12. **[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* 13. **[Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation](https://md.research.elamur.ai/free/en/papers/wei-2026-mirroring-users-building-preference-aligned/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianjun Wei, Huizhong Guo, Yingpeng Du, et al.*2026 · 1 citation* 14. [**A Survey on Parameter-efficient Fine-tuning of Large Models: Techniques, Trends, and Challenges (大模型参数高效微调方法综述: 技术、趋势与挑战) Acta Automatica Sinica (自动化学报)**Anda Tang (唐岸达), Zhouchen Lin (林宙辰)*2026*](https://md.research.elamur.ai/free/en/papers/tang-2026-parameter-efficient-fine/) 15. **[Calibrated Progressive Distillation: Co-Designing Curriculum and Target Mixing for Knowledge Distillation of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-calibrated-progressive-distillation-co-designing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mengxiang Zhang, Lingyuan Liu*2026* 16. **[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* 17. **[Soft Orthogonal Low-Rank Adaptation for Knowledge Sharing in Large Language Model Continual Learning](https://md.research.elamur.ai/free/en/papers/wang-2026-soft-orthogonal-low-rank-adaptation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yitong Wang, Xue Han, WenChun Gao, et al.*2026* 18. [**SmolKalam: Ensemble Quality-Filtered Translation at Scale for High Quality Arabic Post-Training Data arXiv:2511.18411**Sultan Alrashed, Chadi Helwe, Francesco Orabona*2025*](https://md.research.elamur.ai/free/en/papers/alrashed-2025-smolkalam-ensemble-quality-filtered-translation/) 48 more citing papers of our library have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/authors/shwai-he/) — Shwai He