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 651–675 of 935 papers

TitleStatusHype
Federated Adversarial Learning for Robust Autonomous Landing Runway Detection—0
Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources—0
Federated Low-Rank Adaptation with Differential Privacy over Wireless Networks—0
FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning—0
FedMCP: Parameter-Efficient Federated Learning with Model-Contrastive Personalization—0
FedP^2EFT: Federated Learning to Personalize Parameter Efficient Fine-Tuning for Multilingual LLMs—0
FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation Models with Mobile Edge Computing—0
FedPIA -- Permuting and Integrating Adapters leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning—0
FedSCA: Federated Tuning with Similarity-guided Collaborative Aggregation for Heterogeneous Medical Image Segmentation—0
FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models—0
FeTT: Continual Class Incremental Learning via Feature Transformation Tuning—0
PETA: Parameter-Efficient Trojan Attacks—0
Few-Shot Adversarial Low-Rank Fine-Tuning of Vision-Language Models—0
FineCLIPER: Multi-modal Fine-grained CLIP for Dynamic Facial Expression Recognition with AdaptERs—0
FINE: Factorizing Knowledge for Initialization of Variable-sized Diffusion Models—0
Fine-tuning vision foundation model for crack segmentation in civil infrastructures—0
Fine Tuning without Catastrophic Forgetting via Selective Low Rank Adaptation—0
FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications—0
FinSQL: Model-Agnostic LLMs-based Text-to-SQL Framework for Financial Analysis—0
FISH-Tuning: Enhancing PEFT Methods with Fisher Information—0
Flat-LoRA: Low-Rank Adaption over a Flat Loss Landscape—0
FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models—0
From Text to Emoji: How PEFT-Driven Personality Manipulation Unleashes the Emoji Potential in LLMs—0
From Words to Worth: Newborn Article Impact Prediction with LLM—0
Functional-level Uncertainty Quantification for Calibrated Fine-tuning on 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