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 451–475 of 935 papers

TitleStatusHype
Exploring Adapter Design Tradeoffs for Low Resource Music Generation—0
Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning—0
Exploring Zero and Few-shot Techniques for Intent Classification—0
External Prompt Features Enhanced Parameter-efficient Fine-tuning for Salient Object Detection—0
F^3OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics—0
F^3OCUS - Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics—0
FairLoRA: Unpacking Bias Mitigation in Vision Models with Fairness-Driven Low-Rank Adaptation—0
FastEdit: Fast Text-Guided Single-Image Editing via Semantic-Aware Diffusion Fine-Tuning—0
Fast-NTK: Parameter-Efficient Unlearning for Large-Scale Models—0
FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition—0
Federated Adapter on Foundation Models: An Out-Of-Distribution Approach—0
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
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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