🔗 Who cites «UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning»

76 papers of our library cite it (of the 141,840 whose bibliographies we read) · 8 with a page here, the most cited first · ← back to «UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning»

1. LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language ModelsZhiqiang Hu, Lei Wang, Yihuai Lan, et al.2023 · 399 citations
2. Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive SurveyZeyu Han, Chao Gao, Jinyang Liu, et al.2024 · 236 citations
3. DyLoRA: Parameter-Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank AdaptationMojtaba Valipour, Mehdi Rezagholizadeh, Ivan Kobyzev, et al.2023 · 202 citations
4. Reasoning with Language Model Prompting: A SurveyShuofei Qiao, Yixin Ou, Ningyu Zhang, et al.2022 · 161 citations
5. AdaMix: Mixture-of-Adaptations for Parameter-efficient Model TuningYaqing Wang, Sahaj Agarwal, Subhabrata Mukherjee, et al.2022 · 132 citations
6. Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-TuningVladislav Lialin, Vijeta Deshpande, Xiaowei Yao, et al.2023 · 128 citations
7. BLOOM+1: Adding Language Support to BLOOM for Zero-Shot PromptingZheng Xin Yong, Hailey Schoelkopf, Niklas Muennighoff, et al.2023 · 101 citations
8. 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)Anda Tang (唐岸达), Zhouchen Lin (林宙辰)2026

68 more papers of our library cite it; they have no page here yet.

This page in Markdown → https://md.research.elamur.ai/free/en/papers/mao-2022-unipelt-unified-framework-parameter-efficient/cited-by/