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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 171–180 of 935 papers

Title	Date	Tasks	Status	Hype
State-offset Tuning: State-based Parameter-Efficient Fine-Tuning for State Space Models	Mar 5, 2025	parameter-efficient fine-tuningState Space Models	CodeCode Available	1
Addressing Overprescribing Challenges: Fine-Tuning Large Language Models for Medication Recommendation Tasks	Mar 5, 2025	Medical Question Answeringparameter-efficient fine-tuning	CodeCode Available	0
PROPER: A Progressive Learning Framework for Personalized Large Language Models with Group-Level Adaptation	Mar 3, 2025	Mixture-of-Expertsparameter-efficient fine-tuning	—Unverified	0
Re-Imagining Multimodal Instruction Tuning: A Representation View	Mar 2, 2025	Instruction FollowingMME	CodeCode Available	0
LORENZA: Enhancing Generalization in Low-Rank Gradient LLM Training via Efficient Zeroth-Order Adaptive SAM	Feb 26, 2025	parameter-efficient fine-tuning	—Unverified	0
CLLoRA: An Approach to Measure the Effects of the Context Length for LLM Fine-Tuning	Feb 26, 2025	Federated LearningLarge Language Model	—Unverified	0
SECURA: Sigmoid-Enhanced CUR Decomposition with Uninterrupted Retention and Low-Rank Adaptation in Large Language Models	Feb 25, 2025	Continual LearningGSM8K	—Unverified	0
Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization Alignment	Feb 24, 2025	image-classificationImage Classification	CodeCode Available	2
ELBA-Bench: An Efficient Learning Backdoor Attacks Benchmark for Large Language Models	Feb 22, 2025	Backdoor AttackIn-Context Learning	—Unverified	0
R-LoRA: Random Initialization of Multi-Head LoRA for Multi-Task Learning	Feb 21, 2025	Multi-Task Learningparameter-efficient fine-tuning	CodeCode Available	1

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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