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 321–330 of 935 papers

Title	Date	Tasks	Status	Hype
Expanding Sparse Tuning for Low Memory Usage	Nov 4, 2024	parameter-efficient fine-tuning	CodeCode Available	1
C2A: Client-Customized Adaptation for Parameter-Efficient Federated Learning	Nov 1, 2024	Federated Learningparameter-efficient fine-tuning	CodeCode Available	1
Dual Low-Rank Adaptation for Continual Learning with Pre-Trained Models	Nov 1, 2024	Continual Learningparameter-efficient fine-tuning	—Unverified	0
Is Multiple Object Tracking a Matter of Specialization?	Nov 1, 2024	AttributeDomain Generalization	—Unverified	0
CleaR: Towards Robust and Generalized Parameter-Efficient Fine-Tuning for Noisy Label Learning	Oct 31, 2024	parameter-efficient fine-tuning	—Unverified	0
Parameter-Efficient Fine-Tuning Medical Multimodal Large Language Models for Medical Visual Grounding	Oct 31, 2024	parameter-efficient fine-tuningVisual Grounding	—Unverified	0
EF-LLM: Energy Forecasting LLM with AI-assisted Automation, Enhanced Sparse Prediction, Hallucination Detection	Oct 30, 2024	Continual LearningHallucination	—Unverified	0
Efficient Adaptation of Pre-trained Vision Transformer via Householder Transformation	Oct 30, 2024	parameter-efficient fine-tuning	—Unverified	0
Exploring Gradient Subspaces: Addressing and Overcoming LoRA's Limitations in Federated Fine-Tuning of Large Language Models	Oct 30, 2024	Federated Learningparameter-efficient fine-tuning	—Unverified	0
MALoRA: Mixture of Asymmetric Low-Rank Adaptation for Enhanced Multi-Task Learning	Oct 30, 2024	Computational EfficiencyMixture-of-Experts	—Unverified	0

Show:10 25 50

← PrevPage 33 of 94Next →

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