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 921–930 of 935 papers

Title	Date	Tasks	Status
Resource-Efficient Transfer Learning From Speech Foundation Model Using Hierarchical Feature Fusion	Nov 4, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Adapter-Based Extension of Multi-Speaker Text-to-Speech Model for New Speakers	Nov 1, 2022	parameter-efficient fine-tuningSpeech Synthesis	—Unverified
Prompting through Prototype: A Prototype-based Prompt Learning on Pretrained Vision-Language Models	Oct 19, 2022	Few-Shot Learningparameter-efficient fine-tuning	—Unverified
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models	Oct 8, 2022	Language ModelingLanguage Modelling	—Unverified
Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks	Oct 7, 2022	parameter-efficient fine-tuning	—Unverified
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

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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