--- title: "Who cites LoRA+: Efficient Low Rank Adaptation of Large Models" description: "80 papers of the Elamur Research library cite «LoRA+: Efficient Low Rank Adaptation of Large Models», found in their bibliographies; the…" canonical: "https://research.elamur.ai/free/en/papers/hayou-2024-lora-efficient-low-rank-adaptation/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/hayou-2024-lora-efficient-low-rank-adaptation/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [LoRA+: Efficient Low Rank Adaptation of Large…](https://md.research.elamur.ai/free/en/papers/hayou-2024-lora-efficient-low-rank-adaptation/) / cited by # Who cites «LoRA+: Efficient Low Rank Adaptation of Large Models» 80 papers of our library cite it (of the 149,439 whose bibliographies we read) · 14 with a page here, the most cited first · [← back to «LoRA+: Efficient Low Rank Adaptation of Large Models»](https://md.research.elamur.ai/free/en/papers/hayou-2024-lora-efficient-low-rank-adaptation/) 1. **[LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://md.research.elamur.ai/free/en/papers/zheng-2024-llamafactory-unified-efficient-fine-tuning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yaowei Zheng, Richong Zhang, Junhao Zhang, et al.*2024 · 1,048 citations* 2. [**Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey arXiv:2403.14608**Zeyu Han, Chao Gao, Jinyang Liu, et al.*2024 · 240 citations*](https://md.research.elamur.ai/free/en/papers/han-2024-parameter-efficient-fine-tuning-large/) 3. [**LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning arXiv:2308.03303**Longteng Zhang, Lin Zhang, Shaohuai Shi, et al.*2023 · 58 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-lora-fa-efficient-effective-low/) 4. **[AraReasoner: Evaluating Reasoning-Based LLMs for Arabic NLP](https://md.research.elamur.ai/free/en/papers/hasanaath-2025-arareasoner-evaluating-reasoning-based-llms/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ahmed Abul Hasanaath, Aisha Alansari, Ahmed Ashraf, et al.*2025 · 8 citations* 5. **[Tiny Budgets, Big Gains: Parameter Placement Strategy in Parameter Super-Efficient Fine-Tuning](https://md.research.elamur.ai/free/en/papers/zhao-2025-tiny-budgets-big-gains-parameter/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jinman Zhao, Xueyan Zhang, Jiaru Li, et al.*2025 · 8 citations* 6. **[Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide](https://md.research.elamur.ai/free/en/papers/szep-2026-fine-tuning-large-language-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Marton Szep, Daniel Rueckert, Rüdiger von Eisenhart-Rothe, et al.*2026 · 7 citations* 7. **[Why Steering Works: Toward a Unified View of Language Model Parameter Dynamics](https://md.research.elamur.ai/free/en/papers/xu-2026-why-steering-works-unified-view/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ziwen Xu, Chenyan WU, Hengyu Sun, et al.*2026 · 1 citation* 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) Acta Automatica Sinica (自动化学报)**Anda Tang (唐岸达), Zhouchen Lin (林宙辰)*2026*](https://md.research.elamur.ai/free/en/papers/tang-2026-parameter-efficient-fine/) 9. **[ELO: Efficient Layer-Specific Optimization for Continual Pretraining of Multilingual LLMs](https://md.research.elamur.ai/free/en/papers/yoo-2026-elo-efficient-layer-specific-optimization/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Hangyeol Yoo, ChangSu Choi, Minjun Kim, et al.*2026* 10. **[Hebbian-Guided Bi-Directional Rank Adaptation for Parameter-Efficient Fine-Tuning](https://md.research.elamur.ai/free/en/papers/zhao-2026-hebbian-guided-bi-directional-rank/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xing Zhao, Liu Yang, Xi-Le Zhao, et al.*2026* 11. **[LeLoRA: Learnable Low-Rank Adaptation of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhou-2026-lelora-learnable-low-rank-adaptation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaoling Zhou, Mingjie Zhang, Zhemg Lee, et al.*2026* 12. **[On the Representation Geometry of LoRA Model Merging](https://md.research.elamur.ai/free/en/papers/lu-2026-representation-geometry-lora-model-merging/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chenyang Lu, Jiaru Li, Jinman Zhao, et al.*2026* 13. **[Specialization without Sparsity: Efficient and Expressive Split-Path Experts for LLM Fine-Tuning](https://md.research.elamur.ai/free/en/papers/liu-2026-specialization-without-sparsity-efficient-expressive/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ganghao Liu, Qin Zhou, Zhe Wang, et al.*2026* 14. **[Ultra-Low-Dimensional Prompt Tuning via Random Projection](https://md.research.elamur.ai/free/en/papers/wu-2026-ultra-low-dimensional-prompt-tuning/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Zijun Wu, Yongchang Hao, Lili Mou*2026* 66 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/hayou-2024-lora-efficient-low-rank-adaptation/) — LoRA+: Efficient Low Rank Adaptation of Large Models