---
title: "Who cites Xingjian Shi — 148 papers of our library"
description: "148 papers of the Elamur Research library cite the works of Xingjian Shi, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/xingjian-shi/cited-by/"
---
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# Who cites Xingjian Shi

150 citations of their works from 148 papers of our library (of the 149,439 whose bibliographies we read) · 19 with a page here, the most cited first · [← Xingjian Shi](https://md.research.elamur.ai/free/en/authors/xingjian-shi/)

1. **[Context-Aware Attentive Knowledge Tracing](https://md.research.elamur.ai/free/en/papers/ghosh-2020-context-aware-attentive-knowledge-tracing/) [ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD)](https://md.research.elamur.ai/free/en/venues/acm-sigkdd-international-conference-on-knowledge-discovery-data-mining-kdd/)**Aritra Ghosh, Neil Heffernan, Andrew S. Lan*2020 · 601 citations*
2. **[Knowledge Tracing: A Survey](https://md.research.elamur.ai/free/en/papers/abdelrahman-2023-knowledge-tracing-survey/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Ghodai Abdelrahman, Qing Wang, Bernardo Nunes*2023 · 480 citations*
3. **[LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models](https://md.research.elamur.ai/free/en/papers/hu-2023-llm-adapters-adapter-family-parameter/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhiqiang Hu, Lei Wang, Yihuai Lan, et al.*2023 · 401 citations*
4. [**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/)
5. [**A self-attentive model for knowledge tracing**Pandey*2019 · 213 citations*](https://md.research.elamur.ai/free/en/papers/pandey-2019-self-attentive-model-knowledge-tracing/)
6. [**Aligning Large Language Models with Human: A Survey arXiv:2307.12966**Yufei Wang, Wanjun Zhong, Liangyou Li, et al.*2023 · 202 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-aligning-large-language-models-human/)
7. [**Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast arXiv:2211.02556**Kaifeng Bi, Lingxi Xie, Hengheng Zhang, et al.*2022 · 133 citations*](https://md.research.elamur.ai/free/en/papers/bi-2022-pangu-weather-3d-high/)
8. [**Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning arXiv:2303.15647**Vladislav Lialin, Vijeta Deshpande, Xiaowei Yao, et al.*2023 · 129 citations*](https://md.research.elamur.ai/free/en/papers/lialin-2023-scaling-down-scale-up-guide/)
9. **[Unlearn What You Want to Forget: Efficient Unlearning for LLMs](https://md.research.elamur.ai/free/en/papers/chen-2023-unlearn-what-you-want-forget/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiaao Chen, Diyi Yang*2023 · 124 citations*
10. [**Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning arXiv:2309.05444**Ted Zadouri, Ahmet Ustun, Arash Ahmadian, et al.*2023 · 69 citations*](https://md.research.elamur.ai/free/en/papers/zadouri-2023-pushing-mixture-experts-limit-extremely/)
11. **[AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning](https://md.research.elamur.ai/free/en/papers/zhou-2024-autopeft-automatic-configuration-search-parameter/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Han Zhou, Xingchen Wan, Ivan Vulić, et al.*2024 · 46 citations*
12. **[Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide](https://md.research.elamur.ai/free/en/papers/szep-2026-fine-tuning-large-language-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Marton Szep, Daniel Rueckert, Rüdiger von Eisenhart-Rothe, et al.*2026 · 7 citations*
13. **[CUFE at StanceEval2024: Arabic Stance Detection with Fine-Tuned Llama-3 Model](https://md.research.elamur.ai/free/en/papers/ibrahim-2024-cufe-stanceeval2024-arabic-stance-detection/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Michael Ibrahim*2024 · 4 citations*
14. **[CUFE at NADI 2024 shared task: Fine-Tuning Llama-3 To Translate From Arabic Dialects To Modern Standard Arabic](https://md.research.elamur.ai/free/en/papers/ibrahim-2024-cufe-nadi-2024-shared-task/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Michael Ibrahim*2024 · 2 citations*
15. **[LLM-KT: Enhancing Large Language Models with Knowledge Tracing via Multi-Level Plug-and-Play Alignment](https://md.research.elamur.ai/free/en/papers/wang-2026-llm-kt-enhancing-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ziwei Wang, Jie Zhou, Qin Chen, et al.*2026 · 1 citation*
16. [**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/)
17. **[Look Within or Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning](https://md.research.elamur.ai/free/en/papers/liu-2026-look-within-or-beyond-theoretical/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**YongKang Liu, Xingle Xu, Ercong Nie, et al.*2026*
18. **[Tracing Mathematical Proficiency Through Problem-Solving Processes](https://md.research.elamur.ai/free/en/papers/park-2026-tracing-mathematical-proficiency-through-problem/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jungyang Park, Suho Kang, Jaewoo Park, et al.*2026*
19. **[Review and Performance Comparison of Deep Knowledge Tracing Models (深度知识追踪模型综述和性能比较)](https://md.research.elamur.ai/free/en/papers/wang-2022-performance-comparison-deep/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Yu Wang (王宇), Mengxia Zhu (朱梦霞), Shanghui Yang (杨尚辉), et al.*2022*

129 more citing papers of our library have no page here yet.

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