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 326–350 of 935 papers

TitleStatusHype
EEG-Based Mental Imagery Task Adaptation via Ensemble of Weight-Decomposed Low-Rank Adapters—0
BioInstruct: Instruction Tuning of Large Language Models for Biomedical Natural Language Processing—0
BiLoRA: Almost-Orthogonal Parameter Spaces for Continual Learning—0
ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models—0
Language and Task Arithmetic with Parameter-Efficient Layers for Zero-Shot Summarization—0
LayerNorm: A key component in parameter-efficient fine-tuning—0
Bilevel ZOFO: Bridging Parameter-Efficient and Zeroth-Order Techniques for Efficient LLM Fine-Tuning and Meta-Training—0
Aligner: One Global Token is Worth Millions of Parameters When Aligning Large Language Models—0
BiDoRA: Bi-level Optimization-Based Weight-Decomposed Low-Rank Adaptation—0
Know Where You're Going: Meta-Learning for Parameter-Efficient Fine-Tuning—0
A Hessian-informed hyperparameter optimization for differential learning rate—0
Dual Low-Rank Adaptation for Continual Learning with Pre-Trained Models—0
KerZOO: Kernel Function Informed Zeroth-Order Optimization for Accurate and Accelerated LLM Fine-Tuning—0
Dual Decomposition of Weights and Singular Value Low Rank Adaptation—0
Keep the Balance: A Parameter-Efficient Symmetrical Framework for RGB+X Semantic Segmentation—0
L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models—0
DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation—0
Beyond LoRA: Exploring Efficient Fine-Tuning Techniques for Time Series Foundational Models—0
Adapter-Based Extension of Multi-Speaker Text-to-Speech Model for New Speakers—0
Tensor Train Low-rank Approximation (TT-LoRA): Democratizing AI with Accelerated LLMs—0
BeamLoRA: Beam-Constraint Low-Rank Adaptation—0
Ahead-of-Time P-Tuning—0
LACoS-BLOOM: Low-rank Adaptation with Contrastive objective on 8 bits Siamese-BLOOM—0
Learning to Route for Dynamic Adapter Composition in Continual Learning with Language Models—0
LLMI3D: Empowering LLM with 3D Perception from a Single 2D Image—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