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 911–920 of 935 papers

Title	Date	Tasks	Status	Hype
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models	Oct 8, 2022	Language ModelingLanguage Modelling	—Unverified	0
Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks	Oct 7, 2022	parameter-efficient fine-tuning	—Unverified	0
Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning	Oct 4, 2022	Federated Learningparameter-efficient fine-tuning	—Unverified	0
Efficient Few-Shot Learning Without Prompts	Sep 22, 2022	Few-Shot LearningFew-Shot Text Classification	CodeCode Available	4
Meta-Adapters: Parameter Efficient Few-shot Fine-tuning through Meta-Learning	Sep 19, 2022	Few-Shot LearningMeta-Learning	CodeCode Available	0
ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning	Jun 27, 2022	Action ClassificationAction Recognition	CodeCode Available	1
Know Where You're Going: Meta-Learning for Parameter-Efficient Fine-Tuning	May 25, 2022	Cross-Lingual NERLanguage Modeling	—Unverified	0
AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning	May 24, 2022	Natural Language Understandingparameter-efficient fine-tuning	CodeCode Available	1
When does Parameter-Efficient Transfer Learning Work for Machine Translation?	May 23, 2022	Machine Translationparameter-efficient fine-tuning	CodeCode Available	0
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning	May 11, 2022	Few-Shot Text ClassificationIn-Context Learning	CodeCode Available	4

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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