parameter-efficient fine-tuning

Parameter-Efficient Fine-Tuning (PEFT) is a technique used to adapt pre-trained models to new tasks with minimal changes to the model's parameters. This approach is particularly useful in scenarios where computational resources are limited or when it is desirable to maintain the original model's performance on the initial task.

Papers

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Showing 601–610 of 935 papers

Title	Date	Tasks	Status
DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation	May 10, 2024	Federated LearningNatural Language Understanding	—Unverified
Dual Decomposition of Weights and Singular Value Low Rank Adaptation	May 20, 2025	GSM8KMMLU	—Unverified
Dual Low-Rank Adaptation for Continual Learning with Pre-Trained Models	Nov 1, 2024	Continual Learningparameter-efficient fine-tuning	—Unverified
EEG-Based Mental Imagery Task Adaptation via Ensemble of Weight-Decomposed Low-Rank Adapters	Dec 8, 2024	Domain AdaptationEEG	—Unverified
Efficiency at Scale: Investigating the Performance of Diminutive Language Models in Clinical Tasks	Feb 16, 2024	Decision Makingparameter-efficient fine-tuning	—Unverified
Efficiency in Focus: LayerNorm as a Catalyst for Fine-tuning Medical Visual Language Pre-trained Models	Apr 25, 2024	Medical Visual Question Answeringparameter-efficient fine-tuning	—Unverified
Efficient Adaptation For Remote Sensing Visual Grounding	Mar 29, 2025	parameter-efficient fine-tuningVisual Grounding	—Unverified
Efficient Adaptation of Pre-trained Vision Transformer via Householder Transformation	Oct 30, 2024	parameter-efficient fine-tuning	—Unverified
Efficient Adaptation of Pre-trained Vision Transformer underpinned by Approximately Orthogonal Fine-Tuning Strategy	Jul 17, 2025	image-classificationImage Classification	—Unverified
Efficient and Effective Adaptation of Multimodal Foundation Models in Sequential Recommendation	Nov 5, 2024	GPUparameter-efficient fine-tuning	—Unverified

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All datasets BoolQ HellaSwag WinoGrande

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	LLaMA2-7b	Accuracy (% )	82.63	—	Unverified
2	LLaMA2-7b	Accuracy (% )	82.63	—	Unverified
3	LLaMA2-7b	Accuracy (% )	81.93	—	Unverified
4	LLaMA2-7b	Accuracy (% )	80.28	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LLaMA2-7b	Accuracy (% )	76.68	—	Unverified
2	LLaMA2-7b	Accuracy (% )	76.67	—	Unverified
3	LLaMA2-7b	Accuracy (% )	76.27	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LLaMA2-7b	Accuracy (% )	70.8	—	Unverified
2	LLaMA2-7b	Accuracy (% )	70.09	—	Unverified
3	LLaMA2-7b	Accuracy (% )	69.85	—	Unverified