SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 501–525 of 935 papers

TitleStatusHype
MoDE: Effective Multi-task Parameter Efficient Fine-Tuning with a Mixture of Dyadic Experts—0
Leveraging Parameter Efficient Training Methods for Low Resource Text Classification: A Case Study in Marathi—0
SARA: Singular-Value Based Adaptive Low-Rank Adaption—0
Memory-Efficient Orthogonal Fine-Tuning with Principal Subspace Adaptation—0
6G WavesFM: A Foundation Model for Sensing, Communication, and Localization—0
A Comprehensive Evaluation of Large Language Models on Aspect-Based Sentiment Analysis—0
Activation Control for Efficiently Eliciting Long Chain-of-thought Ability of Language Models—0
AdaFish: Fast low-rank parameter-efficient fine-tuning by using second-order information—0
Adapter-Based Extension of Multi-Speaker Text-to-Speech Model for New Speakers—0
Adapter-X: A Novel General Parameter-Efficient Fine-Tuning Framework for Vision—0
Adapting Segment Anything Model to Melanoma Segmentation in Microscopy Slide Images—0
Adaptive Layer Selection for Efficient Vision Transformer Fine-Tuning—0
Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices—0
Adaptive parameter-efficient fine-tuning via Hessian-informed subset selection—0
Adaptive Rank Allocation for Federated Parameter-Efficient Fine-Tuning of Language Models—0
AdaViPro: Region-based Adaptive Visual Prompt for Large-Scale Models Adapting—0
A Decade of Wheat Mapping for Lebanon—0
AdUE: Improving uncertainty estimation head for LoRA adapters in LLMs—0
Advancing Enterprise Spatio-Temporal Forecasting Applications: Data Mining Meets Instruction Tuning of Language Models For Multi-modal Time Series Analysis in Low-Resource Settings—0
A Fine-tuning Enhanced RAG System with Quantized Influence Measure as AI Judge—0
AFLoRA: Adaptive Freezing of Low Rank Adaptation in Parameter Efficient Fine-Tuning of Large Models—0
A GEN AI Framework for Medical Note Generation—0
Ahead-of-Time P-Tuning—0
A Hessian-informed hyperparameter optimization for differential learning rate—0
Aligner: One Global Token is Worth Millions of Parameters When Aligning Large Language Models—0
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All datasetsBoolQHellaSwagWinoGrande

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LLaMA2-7bAccuracy (% )82.63—Unverified
2LLaMA2-7bAccuracy (% )82.63—Unverified
3LLaMA2-7bAccuracy (% )81.93—Unverified
4LLaMA2-7bAccuracy (% )80.28—Unverified
#ModelMetricClaimedVerifiedStatus
1LLaMA2-7bAccuracy (% )76.68—Unverified
2LLaMA2-7bAccuracy (% )76.67—Unverified
3LLaMA2-7bAccuracy (% )76.27—Unverified
#ModelMetricClaimedVerifiedStatus
1LLaMA2-7bAccuracy (% )70.8—Unverified
2LLaMA2-7bAccuracy (% )70.09—Unverified
3LLaMA2-7bAccuracy (% )69.85—Unverified