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 301–325 of 935 papers

TitleStatusHype
Permissioned LLMs: Enforcing Access Control in Large Language Models—0
LoFT: Low-Rank Adaptation That Behaves Like Full Fine-Tuning—0
DLP: Dynamic Layerwise Pruning in Large Language ModelsCode0
Parameter-Efficient Fine-Tuning with Column Space Projection—0
UORA: Uniform Orthogonal Reinitialization Adaptation in Parameter-Efficient Fine-Tuning of Large Models—0
Optimization-Inspired Few-Shot Adaptation for Large Language Models—0
KerZOO: Kernel Function Informed Zeroth-Order Optimization for Accurate and Accelerated LLM Fine-Tuning—0
HD-PiSSA: High-Rank Distributed Orthogonal Adaptation—0
AuroRA: Breaking Low-Rank Bottleneck of LoRA with Nonlinear Mapping—0
CLaDMoP: Learning Transferrable Models from Successful Clinical Trials via LLMsCode0
Activation Control for Efficiently Eliciting Long Chain-of-thought Ability of Language Models—0
Representation Discrepancy Bridging Method for Remote Sensing Image-Text Retrieval—0
Explain Less, Understand More: Jargon Detection via Personalized Parameter-Efficient Fine-tuning—0
4,500 Seconds: Small Data Training Approaches for Deep UAV Audio ClassificationCode0
AdUE: Improving uncertainty estimation head for LoRA adapters in LLMs—0
15,500 Seconds: Lean UAV Classification Leveraging PEFT and Pre-Trained NetworksCode0
Parameter-Efficient Fine-Tuning of Multispectral Foundation Models for Hyperspectral Image Classification—0
Few-Shot Adversarial Low-Rank Fine-Tuning of Vision-Language Models—0
CoLA: Collaborative Low-Rank AdaptationCode0
VP Lab: a PEFT-Enabled Visual Prompting Laboratory for Semantic Segmentation—0
Dual Decomposition of Weights and Singular Value Low Rank Adaptation—0
Privacy Preserving Conversion Modeling in Data Clean Room—0
OSoRA: Output-Dimension and Singular-Value Initialized Low-Rank Adaptation—0
Efficient Federated Class-Incremental Learning of Pre-Trained Models via Task-agnostic Low-rank Residual Adaptation—0
SRLoRA: Subspace Recomposition in Low-Rank Adaptation via Importance-Based Fusion and Reinitialization—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