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 476–500 of 935 papers

TitleStatusHype
UORA: Uniform Orthogonal Reinitialization Adaptation in Parameter-Efficient Fine-Tuning of Large Models—0
User-Specific Dialogue Generation with User Profile-Aware Pre-Training Model and Parameter-Efficient Fine-Tuning—0
Using Few-Shot Learning to Classify Primary Lung Cancer and Other Malignancy with Lung Metastasis in Cytological Imaging via Endobronchial Ultrasound Procedures—0
VHDL-Eval: A Framework for Evaluating Large Language Models in VHDL Code Generation—0
Vision-Centric Representation-Efficient Fine-Tuning for Robust Universal Foreground Segmentation—0
Vision-Language Model-Based Semantic-Guided Imaging Biomarker for Early Lung Cancer Detection—0
Visual Cue Enhancement and Dual Low-Rank Adaptation for Efficient Visual Instruction Fine-Tuning—0
Visual Variational Autoencoder Prompt Tuning—0
VP Lab: a PEFT-Enabled Visual Prompting Laboratory for Semantic Segmentation—0
VP-NTK: Exploring the Benefits of Visual Prompting in Differentially Private Data Synthesis—0
Watch and Learn: Leveraging Expert Knowledge and Language for Surgical Video Understanding—0
Weak-to-Strong Backdoor Attack for Large Language Models—0
WeightLoRA: Keep Only Necessary Adapters—0
Weight Spectra Induced Efficient Model Adaptation—0
What Lurks Within? Concept Auditing for Shared Diffusion Models at Scale—0
Exploring Gradient Subspaces: Addressing and Overcoming LoRA's Limitations in Federated Fine-Tuning of Large Language Models—0
WIKITIDE: A Wikipedia-Based Timestamped Definition Pairs Dataset—0
WordCon: Word-level Typography Control in Scene Text Rendering—0
X-PEFT: eXtremely Parameter-Efficient Fine-Tuning for Extreme Multi-Profile Scenarios—0
You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models—0
Zero-Shot Adaptation of Parameter-Efficient Fine-Tuning in Diffusion Models—0
Zero-Shot Embeddings Inform Learning and Forgetting with Vision-Language Encoders—0
ELP-Adapters: Parameter Efficient Adapter Tuning for Various Speech Processing Tasks—0
Correcting Negative Bias in Large Language Models through Negative Attention Score Alignment—0
Tensor Train Low-rank Approximation (TT-LoRA): Democratizing AI with Accelerated LLMs—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