🧑‍🔬 Who cites Sungjae Lee

85 citations of their works from 85 papers of our library (of the 149,439 whose bibliographies we read) · 20 with a page here, the most cited first · ← Sungjae Lee

1. QLoRA: Efficient Finetuning of Quantized LLMsConference on Neural Information Processing Systems (NeurIPS)Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, et al.2023 · 2,929 citations
2. FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPUarXiv:2303.06865Ying Sheng, Lianmin Zheng, Binhang Yuan, et al.2023 · 402 citations
3. A Survey on Model Compression for Large Language ModelsTransactions of the Association for Computational Linguistics (TACL)Xunyu Zhu, Jian Li, Yong Liu, et al.2024 · 381 citations
4. Challenges and Applications of Large Language ModelsarXiv:2307.10169Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.2023 · 301 citations
5. A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and ValidationArtificial Intelligence ReviewXiaowei Huang, Wenjie Ruan, Wei Huang, et al.2023 · 199 citations
6. Deja Vu: Contextual Sparsity for Efficient LLMs at Inference TimeInternational Conference on Machine Learning (ICML)Zichang Liu, Jue Wang, Tri Dao, et al.2023 · 185 citations
7. Do All Languages Cost the Same? Tokenization in the Era of Commercial Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Orevaoghene Ahia, Sachin Kumar, Hila Gonen, et al.2023 · 174 citations
8. ShortGPT: Layers in Large Language Models are More Redundant Than You ExpectarXiv:2403.03853Xin Men, Mingyu Xu, Qingyu Zhang, et al.2024 · 169 citations
9. OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language ModelsProceedings of the AAAI Conference on Artificial IntelligenceChanghun Lee, Jungyu Jin, Taesu Kim, et al.2023 · 149 citations
10. Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative DecodingAnnual Meeting of the Association for Computational Linguistics (ACL)Jun Zhang, Jue Wang, Huan Li, et al.2024 · 148 citations
11. The case for 4-bit precision: k-bit Inference Scaling LawsarXiv:2212.09720Tim Dettmers, Luke Zettlemoyer2022 · 123 citations
12. A Survey on Efficient Inference for Large Language ModelsarXiv:2404.14294Zixuan Zhou, Xuefei Ning, Ke Hong, et al.2024 · 113 citations
13. Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to SystemsACM Computing SurveysXupeng Miao, Gabriele Oliaro, Zhihao Zhang, et al.2023 · 110 citations
14. Personal LLM Agents: Insights and Survey about the Capability, Efficiency and SecurityarXiv:2401.05459Yuanchun Li, Hao Wen, Weijun Wang, et al.2024 · 100 citations
15. LLM Inference Unveiled: Survey and Roofline Model InsightsarXiv 2024Zhihang Yuan, Yuzhang Shang, Yang Zhou, et al.2024 · 91 citations
16. REST: Retrieval-Based Speculative DecodingarXiv:2311.08252Zhenyu He; Zexuan Zhong; Tianle Cai; Jason D. Lee;, Di He2023 · 87 citations
17. Extreme Compression of Large Language Models via Additive QuantizationInternational Conference on Machine Learning (ICML)Vage Egiazarian, Andrei Panferov, Denis Kuznedelev, et al.2024 · 84 citations
18. “Give Me BF16 or Give Me Death”? Accuracy-Performance Trade-Offs in LLM QuantizationAnnual Meeting of the Association for Computational Linguistics (ACL)Eldar Kurtic, Alexandre Noll Marques, Shubhra Pandit, et al.2025 · 21 citations
19. KNN-SSD: Enabling Dynamic Self-Speculative Decoding via Nearest Neighbor Layer Set OptimizationFindings of the Association for Computational LinguisticsMingbo Song, Heming Xia, Jun Zhang, et al.2026 · 5 citations
20. pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware TrainingFindings of the Association for Computational LinguisticsWenzheng Zhang, Bingzheng Liu2026

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