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 701–725 of 935 papers

TitleStatusHype
Low-Rank Adaption on Transformer-based Oriented Object Detector for Satellite Onboard Processing of Remote Sensing ImagesCode0
Differentially Private Fine-Tuning of Diffusion Models—0
SwitchLoRA: Switched Low-Rank Adaptation Can Learn Full-Rank Information—0
Mamba State-Space Models Are Lyapunov-Stable Learners—0
Spectrum-Aware Parameter Efficient Fine-Tuning for Diffusion ModelsCode0
SAM-E: Leveraging Visual Foundation Model with Sequence Imitation for Embodied Manipulation—0
Domain-Inspired Sharpness-Aware Minimization Under Domain ShiftsCode0
MemControl: Mitigating Memorization in Diffusion Models via Automated Parameter SelectionCode0
Parameter-efficient Fine-tuning in Hyperspherical Space for Open-vocabulary Semantic Segmentation—0
RAP: Efficient Text-Video Retrieval with Sparse-and-Correlated Adapter—0
IAPT: Instruction-Aware Prompt Tuning for Large Language Models—0
Semantic are Beacons: A Semantic Perspective for Unveiling Parameter-Efficient Fine-Tuning in Knowledge Learning—0
Sparsity- and Hybridity-Inspired Visual Parameter-Efficient Fine-Tuning for Medical Diagnosis—0
DoRA: Enhancing Parameter-Efficient Fine-Tuning with Dynamic Rank DistributionCode0
Trans-LoRA: towards data-free Transferable Parameter Efficient Finetuning—0
Self-Corrected Multimodal Large Language Model for End-to-End Robot Manipulation—0
PatchProt: Hydrophobic patch prediction using protein foundation modelsCode0
BiSup: Bidirectional Quantization Error Suppression for Large Language Models—0
Prompt Tuning Strikes Back: Customizing Foundation Models with Low-Rank Prompt AdaptationCode0
Pre-Trained Vision-Language Models as Partial Annotators—0
FeTT: Continual Class Incremental Learning via Feature Transformation Tuning—0
HARIS: Human-Like Attention for Reference Image Segmentation—0
SPD-CFL: Stepwise Parameter Dropout for Efficient Continual Federated Learning—0
Tell Me Why: Explainable Public Health Fact-Checking with Large Language ModelsCode0
DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation—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