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 151–175 of 935 papers

TitleStatusHype
FedSCA: Federated Tuning with Similarity-guided Collaborative Aggregation for Heterogeneous Medical Image Segmentation—0
Empowering Smaller Models: Tuning LLaMA and Gemma with Chain-of-Thought for Ukrainian Exam TasksCode1
MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts—0
Quantum-Enhanced LLM Efficient Fine Tuning—0
A Survey on Federated Fine-tuning of Large Language ModelsCode2
Watch and Learn: Leveraging Expert Knowledge and Language for Surgical Video Understanding—0
MoLEx: Mixture of Layer Experts for Finetuning with Sparse UpcyclingCode0
Rethinking Few-Shot Adaptation of Vision-Language Models in Two StagesCode1
Enhancing Aviation Communication Transcription: Fine-Tuning Distil-Whisper with LoRA—0
Singular Value Fine-tuning for Few-Shot Class-Incremental Learning—0
Efficient Federated Fine-Tuning of Large Language Models with Layer Dropout—0
Privacy-Preserved Automated Scoring using Federated Learning for Educational ResearchCode0
Enhanced Continual Learning of Vision-Language Models with Model Fusion—0
Revisiting semi-supervised learning in the era of foundation modelsCode1
Project-Probe-Aggregate: Efficient Fine-Tuning for Group Robustness—0
MoFE: Mixture of Frozen Experts Architecture—0
Lifelong Learning with Task-Specific Adaptation: Addressing the Stability-Plasticity Dilemma—0
Personalized Text Generation with Contrastive Activation Steering—0
LoRACode: LoRA Adapters for Code Embeddings—0
Personalized Federated Fine-tuning for Heterogeneous Data: An Automatic Rank Learning Approach via Two-Level LoRA—0
State-offset Tuning: State-based Parameter-Efficient Fine-Tuning for State Space ModelsCode1
Addressing Overprescribing Challenges: Fine-Tuning Large Language Models for Medication Recommendation TasksCode0
PROPER: A Progressive Learning Framework for Personalized Large Language Models with Group-Level Adaptation—0
Re-Imagining Multimodal Instruction Tuning: A Representation ViewCode0
LORENZA: Enhancing Generalization in Low-Rank Gradient LLM Training via Efficient Zeroth-Order Adaptive SAM—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