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Who cites Tenghao Huang

415 citations of their works from 415 papers of our library (of the 149,439 whose bibliographies we read) · 82 with a page here, the most cited first · ← Tenghao Huang

  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. Crosslingual Generalization through Multitask FinetuningarXiv:2211.01786Niklas Muennighoff, Thomas Wang, Lintang Sutawika, et al.2022 · 703 citations
  3. The Flan Collection: Designing Data and Methods for Effective Instruction TuningarXiv:2301.13688Shayne Longpre, Le Hou, Tu Vu, et al.2023 · 607 citations
  4. Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!arXiv:2310.03693Xiangyu Qi, Yi Zeng, Tinghao Xie, et al.2023 · 355 citations
  5. Generate rather than Retrieve: Large Language Models are Strong Context GeneratorsarXiv:2209.10063Wenhao Yu, Dan Iter, Shuohang Wang, et al.2022 · 281 citations
  6. LongLoRA: Efficient Fine-tuning of Long-Context Large Language ModelsarXiv:2309.12307Yukang Chen, Shengju Qian, Haotian Tang, et al.2023 · 268 citations
  7. Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive SurveyarXiv:2403.14608Zeyu Han, Chao Gao, Jinyang Liu, et al.2024 · 240 citations
  8. ART: Automatic multi-step reasoning and tool-use for large language modelsarXiv:2303.09014Bhargavi Paranjape, Scott Lundberg, Sameer Singh, et al.2023 · 154 citations
  9. Many-Shot In-Context LearningConference on Neural Information Processing Systems (NeurIPS)Rishabh Agarwal, Avi Singh, Lei M. Zhang, et al.2024 · 142 citations
  10. Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-TuningarXiv:2303.15647Vladislav Lialin, Vijeta Deshpande, Xiaowei Yao, et al.2023 · 129 citations
  11. Efficient Large Language Models: A SurveyarXiv:2312.03863Zhongwei Wan, Xin Wang, Che Liu, et al.2023 · 110 citations
  12. LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA CompositionarXiv:2307.13269Chengsong Huang, Qian Liu, Bill Yuchen Lin, et al.2023 · 108 citations
  13. Personal LLM Agents: Insights and Survey about the Capability, Efficiency and SecurityarXiv:2401.05459Yuanchun Li, Hao Wen, Weijun Wang, et al.2024 · 100 citations
  14. LoRA+: Efficient Low Rank Adaptation of Large ModelsarXiv:2402.12354Soufiane Hayou, Nikhil Ghosh, Bin Yu2024 · 97 citations
  15. S-LoRA: Serving Thousands of Concurrent LoRA AdaptersMLSys 2024Ying Sheng, Shiyi Cao, Dacheng Li, et al.2023 · 80 citations
  16. Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and OpportunitiesarXiv:2408.07666Enneng Yang, Li Shen, Guibing Guo, et al.2024 · 79 citations
  17. Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction TuningarXiv:2309.05444Ted Zadouri, Ahmet Ustun, Arash Ahmadian, et al.2023 · 69 citations
  18. ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language ModelsarXiv:2305.18323Binfeng Xu, Zhiyuan Peng, Bowen Lei, et al.2023 · 61 citations
  19. When does In-context Learning Fall Short and Why? A Study on Specification-Heavy TasksarXiv:2311.08993Hao Peng, Xiaozhi Wang, Jianhui Chen, et al.2023 · 42 citations
  20. InstructAlign: High-and-Low Resource Language Alignment via Continual Crosslingual Instruction TuningarXiv:2305.13627Samuel Cahyawijaya, Holy Lovenia, Tiezheng Yu, et al.2023 · 22 citations
  21. Astraios: Parameter-Efficient Instruction Tuning Code Large Language ModelsarXiv:2401.00788Terry Yue Zhuo, Armel Zebaze, Nitchakarn Suppattarachai, et al.2024 · 21 citations
  22. MoRAL: MoE Augmented LoRA for LLMs' Lifelong LearningarXiv:2402.11260Shu Yang; Muhammad Asif Ali; Cheng-Long Wang; Lijie Hu;, Di Wang2024 · 21 citations
  23. Efficiently Adapting Pretrained Language Models To New LanguagesarXiv:2311.05741Zoltan Csaki, Pian Pawakapan, Urmish Thakker, et al.2023 · 21 citations
  24. Investigating Forgetting in Pre-Trained Representations Through Continual LearningarXiv:2305.05968Yun Luo, Zhen Yang, Xuefeng Bai, et al.2023 · 10 citations
  25. Training LLMs to Recognize Hedges in Dialogues about Roadrunner CartoonsAmie Paige, Adil Soubki, John Murzaku, et al.2024 · 4 citations
  26. Learning from Convenience Samples: A Case Study on Fine-Tuning LLMs for Survey Non-response in the German Longitudinal Election StudyarXiv:2509.25063Tobias Holtdirk, Dennis Assenmacher, Arnim Bleier, et al.2025 · 1 citation
  27. 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
  28. Training LLMs to Recognize Hedges in Spontaneous NarrativesSIGDIAL 2024Amie J. Paige, Adil Soubki, John Murzaku, et al.2024

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