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 551–575 of 935 papers

TitleStatusHype
LoRA as a Flexible Framework for Securing Large Vision Systems—0
LoRACode: LoRA Adapters for Code Embeddings—0
LoRA Diffusion: Zero-Shot LoRA Synthesis for Diffusion Model Personalization—0
LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation—0
LoRA Dropout as a Sparsity Regularizer for Overfitting Control—0
LoRA ensembles for large language model fine-tuning—0
LoRA-FAIR: Federated LoRA Fine-Tuning with Aggregation and Initialization Refinement—0
LoRAGuard: An Effective Black-box Watermarking Approach for LoRAs—0
LoRA-Mini : Adaptation Matrices Decomposition and Selective Training—0
LoRA-X: Bridging Foundation Models with Training-Free Cross-Model Adaptation—0
LORD: Low Rank Decomposition Of Monolingual Code LLMs For One-Shot Compression—0
LORENZA: Enhancing Generalization in Low-Rank Gradient LLM Training via Efficient Zeroth-Order Adaptive SAM—0
LoRTA: Low Rank Tensor Adaptation of Large Language Models—0
LoTR: Low Tensor Rank Weight Adaptation—0
LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits—0
Low-Rank Adaptation of Neural Fields—0
Low-Rank Adapters Meet Neural Architecture Search for LLM Compression—0
Low-rank Attention Side-Tuning for Parameter-Efficient Fine-Tuning—0
LPT++: Efficient Training on Mixture of Long-tailed Experts—0
Parameter-Efficient Continual Fine-Tuning: A Survey—0
Parameter Efficient Continual Learning with Dynamic Low-Rank Adaptation—0
Parameter Efficient Fine Tuning: A Comprehensive Analysis Across Applications—0
Parameter-Efficient Fine-Tuning Design Spaces—0
Parameter-Efficient Fine-Tuning for Continual Learning: A Neural Tangent Kernel Perspective—0
Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey—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