---
title: "Who cites Mingjie Sun — 180 papers of our library"
description: "180 papers of the Elamur Research library cite the works of Mingjie Sun, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/mingjie-sun/cited-by/"
---
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# Who cites Mingjie Sun

181 citations of their works from 180 papers of our library (of the 149,439 whose bibliographies we read) · 36 with a page here, the most cited first · [← Mingjie Sun](https://md.research.elamur.ai/free/en/authors/mingjie-sun/)

1. [**Reducing Transformer Depth on Demand with Structured Dropout arXiv:1909.11556**Angela Fan, Edouard Grave, Armand Joulin*2019 · 560 citations*](https://md.research.elamur.ai/free/en/papers/fan-2019-reducing-transformer-depth-demand-structured/)
2. **[Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning](https://md.research.elamur.ai/free/en/papers/gordon-2020-compressing-bert-studying-effects-weight/) [Workshop on Representation Learning for NLP](https://md.research.elamur.ai/free/en/venues/repl4nlp/)**Mitchell Gordon, Kevin Duh, Nicholas Andrews*2020 · 407 citations*
3. **[FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision](https://md.research.elamur.ai/free/en/papers/shah-2024-flashattention-3-fast-accurate-attention/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jay Shah, Ganesh Bikshandi, Ying Zhang, et al.*2024 · 400 citations*
4. **[Structured Pruning Learns Compact and Accurate Models](https://md.research.elamur.ai/free/en/papers/xia-2022-structured-pruning-learns-compact-accurate/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mengzhou Xia, Zexuan Zhong, Danqi Chen*2022 · 221 citations*
5. [**Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch arXiv:2311.03099**Le Yu, Bowen Yu, Haiyang Yu, et al.*2023 · 217 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-language-models-super-mario-absorbing/)
6. **[Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time](https://md.research.elamur.ai/free/en/papers/liu-2023-deja-vu-contextual-sparsity-efficient/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Zichang Liu, Jue Wang, Tri Dao, et al.*2023 · 185 citations*
7. [**PyramidKV: Dynamic KV Cache Compression based on Pyramidal Information Funneling arXiv:2406.02069**Zefan Cai, Yichi Zhang, Bofei Gao, et al.*2024 · 155 citations*](https://md.research.elamur.ai/free/en/papers/cai-2024-pyramidkv-dynamic-kv-cache-compression/)
8. **[Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding](https://md.research.elamur.ai/free/en/papers/zhang-2024-draft-verify-lossless-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jun Zhang, Jue Wang, Huan Li, et al.*2024 · 148 citations*
9. [**H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models arXiv:2306.14048**Zhenyu Zhang, Ying Sheng, Tianyi Zhou, et al.*2023 · 124 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-h-2-o-heavy-hitter/)
10. **[Learning Language Specific Sub-network for Multilingual Machine Translation](https://md.research.elamur.ai/free/en/papers/lin-2021-learning-language-specific-sub-network/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zehui Lin, Liwei Wu, Mingxuan Wang, et al.*2021 · 104 citations*
11. [**Accelerating Sparse Deep Neural Networks arXiv 2021 (NVIDIA)**Asit Mishra, Jorge Albericio Latorre, Jeff Pool, et al.*2021 · 97 citations*](https://md.research.elamur.ai/free/en/papers/mishra-2021-accelerating-sparse-deep-neural-networks/)
12. **[Training Language Models to Reason Efficiently](https://md.research.elamur.ai/free/en/papers/arora-2025-training-language-models-reason-efficiently/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Daman Arora, Andrea Zanette*2025 · 74 citations*
13. [**A Primer on the Inner Workings of Transformer-based Language Models arXiv:2405.00208**Javier Ferrando, Gabriele Sarti, Arianna Bisazza, et al.*2024 · 72 citations*](https://md.research.elamur.ai/free/en/papers/ferrando-2024-primer-inner-workings-transformer-based/)
14. [**MiMo-V2-Flash Technical Report arXiv:2601.02780**Xiaomi LLM-Core Team; Bangjun Xiao; Bingquan Xia; Bo Yang; Bofei Gao; Bowen Shen; Chen Zhang; Chenhong He; Chiheng Lou; Fuli Luo; Gang Wang; Gang Xie; Hailin Zhang; Hanglong Lv; Hanyu Li; Heyu Chen; Hongshen Xu; Houbin Zhang; Huaqiu Liu; Jiangshan Duo; Jianyu Wei; Jiebao Xiao; Jinhao Dong; Jun Shi; Junhao Hu; Kainan Bao; Kang Zhou; Lei Li; Liang Zhao; Linghao Zhang; Peidian Li; Qianli Chen; Shaohui Liu; Shihua Yu; Shijie Cao; Shimao Chen; Shouqiu Yu; Shuo Liu; Tianling Zhou; Weijiang Su; Weikun Wang; Wenhan Ma; Xiangwei Deng; Bohan Mao; Bowen Ye; Can Cai; Chenghua Wang; Chengxuan Zhu; Chong Ma; Chun Chen; Chunan Li; Dawei Zhu; Deshan Xiao; Dong Zhang; Duo Zhang; Fangyue Liu; Feiyu Yang; Fengyuan Shi; Guoan Wang; Hao Tian; Hao Wu; Heng Qu; Hongfei Yi; Hongxu An; Hongyi Guan; Xing Zhang; Yifan Song; Yihan Yan; Yihao Zhao; Yingchun Lai; Yizhao Gao; Yu Cheng; Yuanyuan Tian; Yudong Wang; Zhen Tang; Zhengju Tang; Zhengtao Wen; Zhichao Song; Zhixian Zheng; Zihan Jiang; Jian Wen; Jiarui Sun; Jiawei Li; Jinlong Xue; Jun Xia; Kai Fang; Menghang Zhu; Nuo Chen; Qian Tu; Qihao Zhang; Qiying Wang; Rang Li; Rui Ma; Shaolei Zhang; Shengfan Wang; Shicheng Li; Shuhao Gu; Shuhuai Ren; Sirui Deng; Tao Guo; Tianyang Lu; Weiji Zhuang; Weikang Zhang; Weimin Xiong; Wenshan Huang; Wenyu Yang; Xin Zhang; Xing Yong; Xu Wang; Xueyang Xie; Yilin Jiang; Yixin Yang; Yongzhe He; Yu Tu; Yuanliang Dong; Yuchen Liu; Yue Ma; Yue Yu; Yuxing Xiang; Zhaojun Huang; Zhenru Lin; Zhipeng Xu; Zhiyang Chen; Zhonghua Deng; Zihan Zhang;, Zihao Yue*2026 · 60 citations*](https://md.research.elamur.ai/free/en/papers/team-2026-llm-mimo-v2-flash-technical/)
15. [**DeepSeek: Paradigm Shifts and Technical Evolution in Large AI Models arXiv:2507.09955**Luolin Xiong, Haofen Wang, Xi Chen, et al.*2025 · 41 citations*](https://md.research.elamur.ai/free/en/papers/xiong-2025-deepseek-paradigm-shifts-technical/)
16. **[Unveiling Linguistic Regions in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2024-unveiling-linguistic-regions-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhihao Zhang, Jun Zhao, Qi Zhang, et al.*2024 · 39 citations*
17. [**MiniCPM4: Ultra-Efficient LLMs on End Devices arXiv:2506.07900**MiniCPM Team, Chaojun Xiao, Yuxuan Li, et al.*2025 · 22 citations*](https://md.research.elamur.ai/free/en/papers/minicpm-team-2025-minicpm4-ultra-efficient-llms/)
18. [**ModuleFormer: Modularity Emerges from Mixture-of-Experts arXiv:2306.04640**Yikang Shen, Zheyu Zhang, Tianyou Cao, et al.*2023 · 21 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-moduleformer-modularity-emerges-mixture-experts/)
19. **[Shallow Focus, Deep Fixes: Enhancing Shallow Layers Vision Attention Sinks to Alleviate Hallucination in LVLMs](https://md.research.elamur.ai/free/en/papers/zhang-2025-shallow-focus-deep-fixes-enhancing/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xiaofeng Zhang, Yihao Quan, Chen Shen, et al.*2025 · 14 citations*
20. **[Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-locate-steer-improve-practical-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hengyuan Zhang, Zhihao Zhang, Ercong Nie, et al.*2026 · 9 citations*
21. **[Softpick: No Attention Sink, No Massive Activations with Rectified Softmax](https://md.research.elamur.ai/free/en/papers/zuhri-2026-softpick-no-attention-sink-no/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zayd Muhammad Kawakibi Zuhri, Erland Hilman Fuadi, Alham Fikri Aji*2026 · 6 citations*
22. **[Attention Sinks in Diffusion Language Models](https://md.research.elamur.ai/free/en/papers/rulli-2026-attention-sinks-diffusion-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Maximo Eduardo Rulli, Simone Petruzzi, Edoardo Michielon, et al.*2026 · 5 citations*
23. **[Revealing and Enhancing Core Visual Regions: Harnessing Internal Attention Dynamics for Hallucination Mitigation in LVLMs](https://md.research.elamur.ai/free/en/papers/lyu-2026-revealing-enhancing-core-visual-regions/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Guangtao Lyu, Qi Liu, Chenghao Xu, et al.*2026 · 5 citations*
24. **[Bit-by-Bit: Progressive QAT Strategy with Outlier Channel Splitting for Stable Low-Bit LLMs](https://md.research.elamur.ai/free/en/papers/xu-2026-bit-bit-progressive-qat-strategy/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Binxing Xu, Hao Gu, Lujun Li, et al.*2026 · 3 citations*
25. **[Lizard: An Efficient Linearization Framework for Large Language Models](https://md.research.elamur.ai/free/en/papers/nguyen-2026-lizard-efficient-linearization-framework-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chien Van Nguyen, Huy Huu Nguyen, Ruiyi Zhang, et al.*2026 · 2 citations*
26. **[Attention Sinks Are Provably Necessary in Softmax Transformers: Evidence from Trigger-Conditional Tasks](https://md.research.elamur.ai/free/en/papers/milo-2026-attention-sinks-are-provably-necessary/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuval Ran-Milo*2026 · 1 citation*
27. **[Mid-Think: Training-Free Intermediate-Budget Reasoning via Token-Level Triggers](https://md.research.elamur.ai/free/en/papers/yang-2026-mid-think-training-free-intermediate/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Van Yang, Shouren Wang, Debargha Ganguly, et al.*2026 · 1 citation*
28. **[ReasoningGuard: Safeguarding Large Reasoning Models with Inference-time Safety Aha Moments](https://md.research.elamur.ai/free/en/papers/wang-2026-reasoningguard-safeguarding-large-reasoning-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuquan Wang, Mi Zhang, Yining Wang, et al.*2026 · 1 citation*
29. **[Embracing Anisotropy: Turning Massive Activations into Interpretable Control Knobs for Large Language Models](https://md.research.elamur.ai/free/en/papers/roh-2026-embracing-anisotropy-turning-massive-activations/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Youngji Roh, Hyunjin Cho, Jaehyung Kim*2026*
30. **[Exploring Attention Attractors in Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-exploring-attention-attractors-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ziheng Wang, Zihao Yue, Wenxuan Wang, et al.*2026*
31. **[Latent Attention Denoising: A Training-Free Energy-Based Framework for Mitigating Hallucinations in Vision-Language Models](https://md.research.elamur.ai/free/en/papers/luo-2026-latent-attention-denoising-training-free/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiwen Luo, Siyu Jiang, Weilong Jiang, et al.*2026*
32. **[LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation](https://md.research.elamur.ai/free/en/papers/song-2026-lbllm-lightweight-binarization-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Siqing Song, Chuang Wang, Yong Lang, et al.*2026*
33. **[Noise Steering for Controlled Text Generation: Improving Diversity and Reading-Level Fidelity in Arabic Educational Story Generation](https://md.research.elamur.ai/free/en/papers/khalid-2026-noise-steering-controlled-text-generation/) [Workshop on Innovative Use of NLP for Building Educational Applications (BEA)](https://md.research.elamur.ai/free/en/venues/bea/)**Haziq Khalid, Salsabeel Shapsough, Imran Zualkernan*2026*
34. **[pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training](https://md.research.elamur.ai/free/en/papers/zhang-2026-pquant-effective-low-bit-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenzheng Zhang, Bingzheng Liu*2026*
35. **[SPIDE: Serial and Parallel Intertwined Speculative Decoding](https://md.research.elamur.ai/free/en/papers/xu-2026-spide-serial-parallel-intertwined-speculative/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenru Xu, Peixuan Xu, Ziqi Yang, et al.*2026*
36. **[TokenPenalty: Alleviating Attention Sinks and Positional Decay in LVLMs](https://md.research.elamur.ai/free/en/papers/zhang-2026-tokenpenalty-alleviating-attention-sinks-positional/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaofeng Zhang, Yuanchao Zhu, Qiyan Zhao, et al.*2026*

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

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