---
title: "Who cites LoRA-FA: Efficient and Effective — 47 citing papers"
description: "47 papers of the Elamur Research library cite «LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning», found in their…"
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---
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# Who cites «LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning»

47 papers of our library cite it (of the 149,439 whose bibliographies we read) · 9 with a page here, the most cited first · [← back to «LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning»](https://md.research.elamur.ai/free/en/papers/zhang-2023-lora-fa-efficient-effective-low/)

1. [**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/)
2. [**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/)
3. [**LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition arXiv:2307.13269**Chengsong Huang, Qian Liu, Bill Yuchen Lin, et al.*2023 · 108 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-lorahub-efficient-cross-task-generalization/)
4. [**VeRA: Vector-based Random Matrix Adaptation arXiv:2310.11454**Dawid J. Kopiczko, Tijmen Blankevoort, Yuki M. Asano*2023 · 99 citations*](https://md.research.elamur.ai/free/en/papers/kopiczko-2023-vera-vector-based-random-matrix/)
5. **[RoCoFT: Efficient Finetuning of Large Language Models with Row-Column Updates](https://md.research.elamur.ai/free/en/papers/kowsher-2025-rocoft-efficient-finetuning-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Md Kowsher, Tara Esmaeilbeig, Chun-Nam Yu, et al.*2025 · 5 citations*
6. **[Adapting Where It Matters: Depth-Aware Adaptation for Efficient Multilingual Speech Recognition in Low-Resource Languages](https://md.research.elamur.ai/free/en/papers/xiao-2026-adapting-where-it-matters-depth/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yang Xiao, Eun-Jung Holden, Ting Dang*2026*
7. **[DPLoRA: A Dual-Pruning Framework based on ILP Optimization and Progressive Pruning for Parameter-Efficient LoRA Fine-Tuning](https://md.research.elamur.ai/free/en/papers/park-2026-dplora-dual-pruning-framework-based/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Changjun Park, Sejong Yoon, Jaekwang Kim*2026*
8. **[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*
9. **[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*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/zhang-2023-lora-fa-efficient-effective-low/) — LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning