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 926–935 of 935 papers

Title	Date	Tasks	Status
Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning	Oct 4, 2022	Federated Learningparameter-efficient fine-tuning	—Unverified
Meta-Adapters: Parameter Efficient Few-shot Fine-tuning through Meta-Learning	Sep 19, 2022	Few-Shot LearningMeta-Learning	CodeCode Available
Know Where You're Going: Meta-Learning for Parameter-Efficient Fine-Tuning	May 25, 2022	Cross-Lingual NERLanguage Modeling	—Unverified
When does Parameter-Efficient Transfer Learning Work for Machine Translation?	May 23, 2022	Machine Translationparameter-efficient fine-tuning	CodeCode Available
CLIP Models are Few-shot Learners: Empirical Studies on VQA and Visual Entailment	Mar 14, 2022	parameter-efficient fine-tuningQuestion Answering	—Unverified
HyperPELT: Unified Parameter-Efficient Language Model Tuning for Both Language and Vision-and-Language Tasks	Mar 8, 2022	Language ModelingLanguage Modelling	—Unverified
Meta-Adapter: Parameter Efficient Few-Shot Learning through Meta-Learning	Nov 16, 2021	Few-Shot LearningMeta-Learning	—Unverified
DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models	Oct 30, 2021	parameter-efficient fine-tuning	CodeCode Available
BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models	Oct 16, 2021	Language ModelingLanguage Modelling	—Unverified
Style Attuned Pre-training and Parameter Efficient Fine-tuning for Spoken Language Understanding	Oct 9, 2020	Domain AdaptationLanguage Modeling	—Unverified

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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