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 521–530 of 935 papers

Title	Date	Tasks	Status	Hype
Fighting Randomness with Randomness: Mitigating Optimisation Instability of Fine-Tuning using Delayed Ensemble and Noisy Interpolation	Jun 18, 2024	Computational EfficiencyData Augmentation	CodeCode Available	0
RoseLoRA: Row and Column-wise Sparse Low-rank Adaptation of Pre-trained Language Model for Knowledge Editing and Fine-tuning	Jun 16, 2024	knowledge editingLanguage Modeling	CodeCode Available	0
ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts	Jun 16, 2024	parameter-efficient fine-tuningTransfer Learning	—Unverified	0
ShareLoRA: Parameter Efficient and Robust Large Language Model Fine-tuning via Shared Low-Rank Adaptation	Jun 16, 2024	Continual LearningGSM8K	CodeCode Available	0
Promoting Data and Model Privacy in Federated Learning through Quantized LoRA	Jun 16, 2024	Federated Learningparameter-efficient fine-tuning	—Unverified	0
Personalized Pieces: Efficient Personalized Large Language Models through Collaborative Efforts	Jun 15, 2024	parameter-efficient fine-tuning	CodeCode Available	1
Parameter-Efficient Active Learning for Foundational models	Jun 13, 2024	Active Learningparameter-efficient fine-tuning	—Unverified	0
PC-LoRA: Low-Rank Adaptation for Progressive Model Compression with Knowledge Distillation	Jun 13, 2024	Knowledge DistillationModel Compression	—Unverified	0
Updating CLIP to Prefer Descriptions Over Captions	Jun 12, 2024	parameter-efficient fine-tuning	CodeCode Available	0
A Survey of Recent Backdoor Attacks and Defenses in Large Language Models	Jun 10, 2024	parameter-efficient fine-tuning	—Unverified	0

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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