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 851–860 of 935 papers

Title	Date	Tasks	Status	Hype
Prompt to be Consistent is Better than Self-Consistent? Few-Shot and Zero-Shot Fact Verification with Pre-trained Language Models	Jun 5, 2023	Fact VerificationIn-Context Learning	CodeCode Available	0
OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models	Jun 4, 2023	parameter-efficient fine-tuningQuantization	CodeCode Available	1
Make Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning	Jun 1, 2023	image-classificationImage Classification	CodeCode Available	1
PreQuant: A Task-agnostic Quantization Approach for Pre-trained Language Models	May 30, 2023	parameter-efficient fine-tuningQuantization	—Unverified	0
Explicit Visual Prompting for Universal Foreground Segmentations	May 29, 2023	Camouflaged Object SegmentationDefocus Blur Detection	CodeCode Available	2
LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning	May 28, 2023	Model CompressionNetwork Pruning	CodeCode Available	1
Parameter-Efficient Fine-Tuning without Introducing New Latency	May 26, 2023	Federated Learningparameter-efficient fine-tuning	CodeCode Available	0
Neural Architecture Search for Parameter-Efficient Fine-tuning of Large Pre-trained Language Models	May 26, 2023	Neural Architecture Searchparameter-efficient fine-tuning	—Unverified	0
Towards Adaptive Prefix Tuning for Parameter-Efficient Language Model Fine-tuning	May 24, 2023	Language ModelingLanguage Modelling	—Unverified	0
QLoRA: Efficient Finetuning of Quantized LLMs	May 23, 2023	ChatbotGPU	CodeCode Available	6

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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