---
title: "Who cites Zhaoyang Zhang — 193 papers of our library"
description: "193 papers of the Elamur Research library cite the works of Zhaoyang Zhang, found in their bibliographies; the most cited first."
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---
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# Who cites Zhaoyang Zhang

193 citations of their works from 193 papers of our library (of the 149,439 whose bibliographies we read) · 32 with a page here, the most cited first · [← Zhaoyang Zhang](https://md.research.elamur.ai/free/en/authors/zhaoyang-zhang/)

1. **[A Survey on Model Compression for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhu-2024-survey-model-compression-large-language/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Xunyu Zhu, Jian Li, Yong Liu, et al.*2024 · 381 citations*
2. **[KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization](https://md.research.elamur.ai/free/en/papers/hooper-2024-kvquant-towards-10-million-context/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh, et al.*2024 · 294 citations*
3. **[QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs](https://md.research.elamur.ai/free/en/papers/ashkboos-2024-quarot-outlier-free-4-bit/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Saleh Ashkboos, Amirkeivan Mohtashami, Maximilian L. Croci, et al.*2024 · 254 citations*
4. **[LLM in a flash: Efficient Large Language Model Inference with Limited Memory](https://md.research.elamur.ai/free/en/papers/alizadeh-2024-llm-flash-efficient-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Keivan Alizadeh, Seyed Iman Mirzadeh, Dmitry Belenko, et al.*2024 · 193 citations*
5. [**SqueezeLLM: Dense-and-Sparse Quantization arXiv:2306.07629**Sehoon Kim, Coleman Hooper, Amir Gholami, et al.*2023 · 170 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-squeezellm-dense-sparse-quantization/)
6. [**Atom: Low-bit Quantization for Efficient and Accurate LLM Serving MLSys 2024**Yilong Zhao, Chien-Yu Lin, Kan Zhu, et al.*2023 · 156 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-atom-low-bit-quantization-efficient/)
7. [**QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving MLSys 2025**Yujun Lin, Haotian Tang, Shang Yang, et al.*2024 · 120 citations*](https://md.research.elamur.ai/free/en/papers/lin-2024-qserve-w4a8kv4-quantization-system-co/)
8. [**QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks arXiv:2402.04396**Albert Tseng, Jerry Chee, Qingyao Sun, et al.*2024 · 117 citations*](https://md.research.elamur.ai/free/en/papers/tseng-2024-quip-even-better-llm-quantization/)
9. [**A Survey on Efficient Inference for Large Language Models arXiv:2404.14294**Zixuan Zhou, Xuefei Ning, Ke Hong, et al.*2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2024-survey-efficient-inference-large-language/)
10. **[EfficientQAT: Efficient Quantization-Aware Training for Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-efficientqat-efficient-quantization-aware-training/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mengzhao Chen, Wenqi Shao, Peng Xu, et al.*2025 · 82 citations*
11. [**A Survey of Resource-efficient LLM and Multimodal Foundation Models arXiv:2401.08092**Mengwei Xu, Wangsong Yin, Dongqi Cai, et al.*2024 · 54 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-survey-resource-efficient-llm-multimodal/)
12. [**A Survey of On-Policy Distillation for Large Language Models arXiv:2604.00626**Mingyang Song, Mao Zheng*2026 · 41 citations*](https://md.research.elamur.ai/free/en/papers/song-2026-survey-policy-distillation-large-language/)
13. [**Self-Distilled RLVR arXiv:2604.03128**Chenxu Yang, Chuanyu Qin, Qingyi Si, et al.*2026 · 37 citations*](https://md.research.elamur.ai/free/en/papers/yang-2026-self-distilled-rlvr/)
14. [**A Survey of Small Language Models arXiv:2410.20011**Chien Van Nguyen, Xuan Shen, Ryan Aponte, et al.*2024 · 31 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2024-survey-small-language-models/)
15. **[L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models](https://md.research.elamur.ai/free/en/papers/jeon-2025-l4q-parameter-efficient-quantization-aware/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hyesung Jeon, Yulhwa Kim, Jae-Joon Kim*2025 · 15 citations*
16. [**Lightning OPD: Efficient Post-Training for Large Reasoning Models with Offline On-Policy Distillation arXiv:2604.13010**Yecheng Wu, Song Han, Han Cai*2026 · 10 citations*](https://md.research.elamur.ai/free/en/papers/wu-2026-lightning-opd-efficient-post-training/)
17. [**HDPO: Hybrid Distillation Policy Optimization via Privileged Self-Distillation arXiv:2603.23871**Ken Ding*2026 · 8 citations*](https://md.research.elamur.ai/free/en/papers/ding-2026-hdpo-hybrid-distillation-policy-optimization/)
18. **[Exploring Layer-wise Information Effectiveness for Post-Training Quantization in Small Language Models](https://md.research.elamur.ai/free/en/papers/xiao-2026-exploring-layer-wise-information-effectiveness/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**He Xiao, Qingyao Yang, Dirui Xie, et al.*2026 · 5 citations*
19. [**Self-Distilled Policy Gradient arXiv:2606.04036**Yifeng Liu, Shiyuan Zhang, Yifan Zhang, et al.*2026 · 5 citations*](https://md.research.elamur.ai/free/en/papers/liu-2026-self-distilled-policy-gradient/)
20. **[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*
21. **[SharVeT: Similarity-aware Parameter Sharing with Vector-based Tuning for Efficient LLM Compression](https://md.research.elamur.ai/free/en/papers/yun-2026-sharvet-similarity-aware-parameter-sharing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jeongin Yun, Jaeri Lee, Jongjin Kim, et al.*2026 · 1 citation*
22. **[Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats](https://md.research.elamur.ai/free/en/papers/zhao-2026-unleashing-low-bit-inference-ascend/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Pengxiang Zhao, Hui-Ling Zhen, Xing Li, et al.*2026 · 1 citation*
23. **[ACBQ: Adaptive Cross-Block Quantization of Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-acbq-adaptive-cross-block-quantization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hailing Wang, Jianglin Lu, Yitian Zhang, et al.*2026*
24. **[ArcLight: A Lightweight LLM Inference Architecture for Many-Core CPUs](https://md.research.elamur.ai/free/en/papers/xu-2026-arclight-lightweight-llm-inference-architecture/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuzhuang Xu, Xu Han, Yuxuan Li, et al.*2026*
25. **[Benchmarking Post-Training Quantization of Large Language Models under Microscaling Floating Point Formats](https://md.research.elamur.ai/free/en/papers/zhang-2026-benchmarking-post-training-quantization-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Manyi Zhang, Ji-Fu Li, Zhongao Sun, et al.*2026*
26. **[BWLA: Breaking the Barrier of W1AX Post-Training Quantization for LLMs](https://md.research.elamur.ai/free/en/papers/zhao-2026-bwla-breaking-barrier-w1ax-post/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhixiong Zhao, Zukang Xu, Dawei Yang*2026*
27. **[EfficientLLM: Unified Pruning-Aware Pretraining for Auto-Designed Compact Language Models](https://md.research.elamur.ai/free/en/papers/xing-2026-efficientllm-unified-pruning-aware-pretraining/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xingrun Xing, Zheng Liu, Shitao Xiao, et al.*2026*
28. **[FAQ: Mitigating Quantization Error via Regenerating Calibration Data with Family-Aware Quantization](https://md.research.elamur.ai/free/en/papers/xiao-2026-faq-mitigating-quantization-error-regenerating/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Haiyang Xiao, Weiqing Li, Jinyue Guo, et al.*2026*
29. **[Layer-Wise High-Impact Parameter Ratio Optimization in Post-Training Quantization for Large Language Models](https://md.research.elamur.ai/free/en/papers/pham-2026-layer-wise-high-impact-parameter/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Cuong Pham, Anh Dung Hoang, Cuong C. Nguyen, et al.*2026*
30. **[PE-QAT: Parameter-Efficient Quantization-Aware Training for Large Language Models](https://md.research.elamur.ai/free/en/papers/mishra-2026-pe-qat-parameter-efficient-quantization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shresth Mishra*2026*
31. **[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*
32. **[SCVQ: Sparse-Compensated Vector Quantization for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhou-2026-scvq-sparse-compensated-vector-quantization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zixuan Zhou, Yujun Diao, Zicheng Kong, et al.*2026*

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

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