---
title: "Who cites Xiaoxia Wu — 207 papers of our library"
description: "207 papers of the Elamur Research library cite the works of Xiaoxia Wu, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/xiaoxia-wu/cited-by/"
---
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# Who cites Xiaoxia Wu

207 citations of their works from 207 papers of our library (of the 149,439 whose bibliographies we read) · 38 with a page here, the most cited first · [← Xiaoxia Wu](https://md.research.elamur.ai/free/en/authors/xiaoxia-wu/)

1. **[QLoRA: Efficient Finetuning of Quantized LLMs](https://md.research.elamur.ai/free/en/papers/dettmers-2023-qlora-efficient-finetuning-quantized-llms/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, et al.*2023 · 2,929 citations*
2. **[A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,875 citations*
3. **[LLM-Pruner: On the Structural Pruning of Large Language Models](https://md.research.elamur.ai/free/en/papers/ma-2023-llm-pruner-structural-pruning-large/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xinyin Ma, Gongfan Fang, Xinchao Wang*2023 · 452 citations*
4. [**FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU arXiv:2303.06865**Ying Sheng, Lianmin Zheng, Binhang Yuan, et al.*2023 · 402 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2023-flexgen-high-throughput-generative-inference/)
5. **[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*
6. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
7. **[LLM-QAT: Data-Free Quantization Aware Training for Large Language Models](https://md.research.elamur.ai/free/en/papers/liu-2024-llm-qat-data-free-quantization/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zechun Liu, Barlas Oguz, Changsheng Zhao, et al.*2024 · 276 citations*
8. **[A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-safety-trustworthiness-large-language/) [Artificial Intelligence Review](https://md.research.elamur.ai/free/en/venues/artificial-intelligence-review/)**Xiaowei Huang, Wenjie Ruan, Wei Huang, et al.*2023 · 199 citations*
9. **[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*
10. [**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/)
11. [**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/)
12. **[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*
13. [**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/)
14. [**The case for 4-bit precision: k-bit Inference Scaling Laws arXiv:2212.09720**Tim Dettmers, Luke Zettlemoyer*2022 · 123 citations*](https://md.research.elamur.ai/free/en/papers/dettmers-2022-case-4-bit-precision-k/)
15. [**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/)
16. [**Efficient Large Language Models: A Survey arXiv:2312.03863**Zhongwei Wan, Xin Wang, Che Liu, et al.*2023 · 110 citations*](https://md.research.elamur.ai/free/en/papers/wan-2023-efficient-large-language-models-survey/)
17. **[Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems](https://md.research.elamur.ai/free/en/papers/miao-2023-towards-efficient-generative-large-language/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, et al.*2023 · 110 citations*
18. **[LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models](https://md.research.elamur.ai/free/en/papers/park-2022-lut-gemm-quantized-matrix-multiplication/) [International Conference on Learning Representations (ICLR)](https://md.research.elamur.ai/free/en/venues/international-conference-on-learning-representations-iclr/)**Gunho Park, Baeseong Park, Minsub Kim, et al.*2022 · 103 citations*
19. [**Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security arXiv:2401.05459**Yuanchun Li, Hao Wen, Weijun Wang, et al.*2024 · 100 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-personal-llm-agents-insights-survey/)
20. [**LLM Inference Unveiled: Survey and Roofline Model Insights arXiv 2024**Zhihang Yuan, Yuzhang Shang, Yang Zhou, et al.*2024 · 91 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2024-llm-inference-unveiled-survey-roofline/)
21. [**REST: Retrieval-Based Speculative Decoding arXiv:2311.08252**Zhenyu He; Zexuan Zhong; Tianle Cai; Jason D. Lee;, Di He*2023 · 87 citations*](https://md.research.elamur.ai/free/en/papers/he-2023-rest-retrieval-based-speculative-decoding/)
22. **[Extreme Compression of Large Language Models via Additive Quantization](https://md.research.elamur.ai/free/en/papers/egiazarian-2024-extreme-compression-large-language-models/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Vage Egiazarian, Andrei Panferov, Denis Kuznedelev, et al.*2024 · 84 citations*
23. [**S-LoRA: Serving Thousands of Concurrent LoRA Adapters MLSys 2024**Ying Sheng, Shiyi Cao, Dacheng Li, et al.*2023 · 80 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2023-s-lora-serving-thousands-concurrent/)
24. [**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/)
25. **[What is the Role of Small Models in the LLM Era: A Survey](https://md.research.elamur.ai/free/en/papers/chen-2024-role-small-models-llm-era/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Lihu Chen, Gael Varoquaux*2024 · 23 citations*
26. **[Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models](https://md.research.elamur.ai/free/en/papers/qiu-2025-demons-detail-implementing-load-balancing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zihan Qiu, Zeyu Huang, Bo Zheng, et al.*2025 · 21 citations*
27. **[“Give Me BF16 or Give Me Death”? Accuracy-Performance Trade-Offs in LLM Quantization](https://md.research.elamur.ai/free/en/papers/kurtic-2025-give-me-bf16-or-give/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Eldar Kurtic, Alexandre Noll Marques, Shubhra Pandit, et al.*2025 · 21 citations*
28. [**TigerBot: An Open Multilingual Multitask LLM arXiv:2312.08688**Ye Chen, Wei Cai, Liangmin Wu, et al.*2023 · 15 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-tigerbot-open-multilingual-multitask/)
29. [**Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs arXiv:2505.04519**Yehui Tang, Yichun Yin, Yaoyuan Wang, et al.*2025 · 8 citations*](https://md.research.elamur.ai/free/en/papers/tang-2025-pangu-ultra-moe-train/)
30. **[KNN-SSD: Enabling Dynamic Self-Speculative Decoding via Nearest Neighbor Layer Set Optimization](https://md.research.elamur.ai/free/en/papers/song-2026-knn-ssd-enabling-dynamic-self/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mingbo Song, Heming Xia, Jun Zhang, et al.*2026 · 5 citations*
31. [**Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale arXiv:2603.25040**Yicheng Zou, Dongsheng Zhu, Lin Zhu, et al.*2026 · 3 citations*](https://md.research.elamur.ai/free/en/papers/zou-2026-intern-s1-pro-scientific/)
32. **[Beyond Variance: Knowledge-Aware LLM Compression via Fisher-Aligned Subspace Diagnostics](https://md.research.elamur.ai/free/en/papers/shihab-2026-beyond-variance-knowledge-aware-llm/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma*2026 · 1 citation*
33. **[DELTA: Dynamic Layer-Aware Token Attention for Efficient Long-Context Reasoning](https://md.research.elamur.ai/free/en/papers/zarch-2026-delta-dynamic-layer-aware-token/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hossein Entezari Zarch, Lei Gao, Chaoyi Jiang, et al.*2026 · 1 citation*
34. **[Are Large Language Models Economically Viable for Industry Deployment?](https://md.research.elamur.ai/free/en/papers/mohammad-2026-are-large-language-models-economically/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Abdullah Mohammad, Sushant Kumar Ray, Pushkar Arora, et al.*2026*
35. **[CoopQ: Cooperative Game Inspired Layerwise Mixed Precision Quantization for LLMs](https://md.research.elamur.ai/free/en/papers/zhao-2026-coopq-cooperative-game-inspired-layerwise/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Junchen Zhao, Ali Derakhshan, Jayden Hyman, et al.*2026*
36. **[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*
37. **[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*
38. **[Quantizing Whisper: How Design Choices Affect ASR Performance](https://md.research.elamur.ai/free/en/papers/sohler-2026-quantizing-whisper-how-design-choices/) [Speech Language Models in Low-Resource Settings: Performance, Evaluation, and Bias Analysis (SPEAKABLE) @ LREC](https://md.research.elamur.ai/free/en/venues/speakable/)**Arthur Söhler, Julian Irigoyen, Andreas Søeborg Kirkedal*2026*

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

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