| Challenge: | Low-Rank Adaptation (LoRA) is a parameter-efficient fine-tuning method that updates initial weight matrix W0 with a delta matrix W . |
| Approach: | They propose a method that updates initial weight matrix W0 with a delta matrix W consisting of two low-rank matrices A and B. |
| Outcome: | The proposed method maintains a performance on par with LoRA despite the fact that the trainable parameters of CondLoRA are fewer than those of LoRA. |
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Sparsity May Be All You Need: Sparse Random Parameter Adaptation (2025.findings-emnlp)
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| Challenge: | Parameter-Efficient Fine-Tuning (PEFT) methods aim at reducing computational and memory resources for fine-tuning large language models. |
| Approach: | They propose to train on a small number of parameters instead of all model parameters . they compare the method to LoRA and find it to be efficient . |
| Outcome: | The proposed method is competitive with LoRA when using a similar number of trainable parameters. |
PRILoRA: Pruned and Rank-Increasing Low-Rank Adaptation (2024.findings-eacl)
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| Challenge: | Several approaches to parameter-efficient fine-tuning have been proposed . low-rank Adaptation (LoRA) does not consider the varying importance of each layer . |
| Approach: | They propose a method that allocates a different rank for each layer and performs pruning throughout the training process. |
| Outcome: | The proposed method is based on eight GLUE benchmarks and is currently the state of the art. |
GeLoRA: Geometric Adaptive Ranks For Efficient LoRA Fine-tuning (2025.findings-emnlp)
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| Challenge: | Existing adaptive LoRA methods lack a theoretical foundation to guide this trade-off optimally. |
| Approach: | They propose a principled approach that estimates the intrinsic dimensionality of hidden data representations to adaptively select LoRA ranks. |
| Outcome: | Experiments show that GeLoRA outperforms adaptive LoRA methods by up to +1.0% . |
SuLoRA: Subspace Low-Rank Adaptation for Parameter-Efficient Fine-Tuning (2025.findings-acl)
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| Challenge: | Existing methods for fine-tuning large language models (LLMs) introduce parameter interference, leading to a gap in generalization performance for specific tasks compared to full fine-uning. |
| Approach: | They propose a parameter-separated low-rank adapter to account for task differences by decomposing LoRA’s parameter matrix into multiple independent subspaces and assigning them differentially to distinct tasks. |
| Outcome: | The proposed method outperforms LoRA in trainable parameter efficiency and overall model performance on various NLP tasks. |
ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models (2024.naacl-long)
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| Challenge: | Low-rank adaptation (LoRA) has demonstrated commendable performance as a popular method . however, it is implemented with a fixed intrinsic rank that might not be ideal for downstream tasks. |
| Approach: | They propose a method that estimates the importance score of each LoRA rank and prunes abundant LoRA ranks to improve performance. |
| Outcome: | The proposed method outperforms baselines on a variety of tasks with comparable parameters. |
MELoRA: Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-Tuning (2024.acl-long)
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Pengjie Ren, Chengshun Shi, Shiguang Wu, Mengqi Zhang, Zhaochun Ren, Maarten Rijke, Zhumin Chen, Jiahuan Pei
| Challenge: | Large language models (LLMs) are the default paradigm for natural language processing (NLP) as the models’ scale and the diversity of tasks increase, fine-tuning becomes infeasible. |
| Approach: | They propose to freeze original pretrained weights and train a group of mini LoRAs with only a small number of parameters and reduce their rank by 8 times . |
| Outcome: | The proposed model uses fewer trainable parameters while maintaining a higher rank, thereby offering improved performance potential. |
Sensitivity-LoRA : Low-Load Sensitivity-Based Fine-Tuning for Large Language Models (2025.findings-emnlp)
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Hao Zhang, Bo Huang, Zhenjia Li, Xi Xiao, Hui Yi Leong, Zumeng Zhang, Xinwei Long, Tianyang Wang, Hao Xu
| Challenge: | Low-Rank Adaptation (LoRA) is a promising approach to adapting LLMs to specialized tasks . existing rank allocation techniques remain computationally inefficient and unstable . |
| Approach: | They propose a low-rank adapted model that approximates model weight updates using low-ranked decomposition. |
| Outcome: | The proposed method is limited by its uniform rank allocation to each incremental matrix . it leverages the second-order derivatives of the loss function to capture weight sensitivity . |
SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model (2025.naacl-long)
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| Challenge: | Fine-tuning requires substantial computational resources and is prone to overfitting when applied to small datasets. |
| Approach: | They propose a parameter-efficient fine-tuning method that integrates a State Space Model (SSM) to interconnect low-rank matrices. |
| Outcome: | The proposed method achieves comparable performance to LoRA on the general language understanding evaluation (GLUE) benchmark while using only half the parameters. |
LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation (2025.coling-main)
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| Challenge: | Low-Rank Adaptation (LoRA) is currently the most commonly used PEFT method for fine-tuning models with billions of parameters. |
| Approach: | They propose to use low-rank Adaptation to evaluate LoRA parameter features and then retain LoRA for important layers and the other layers share the same LoRA. |
| Outcome: | The proposed method achieves comparable performance to full fine-tuning and LoRA while retaining 50% of the LoRA parameters on average. |
G-LoRA: Global-Local Decoupled Low-Rank Adaptation (2026.findings-acl)
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| Challenge: | Low-Rank Adaptation (LoRA) improves the fine-tuning efficiency and performance of large language models. |
| Approach: | They propose a low-rank adaptive approach that decomposes update matrix into global and local adapters and assigns them to local and global adapters. |
| Outcome: | The proposed method achieves up to 2.7% accuracy improvement over LoRA and its variants on commonsense reasoning, mathematical reasoning, and code generation. |