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 351–400 of 935 papers

TitleStatusHype
Harnessing Generative LLMs for Enhanced Financial Event Entity Extraction Performance—0
ReasoningV: Efficient Verilog Code Generation with Adaptive Hybrid Reasoning ModelCode0
PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models—0
6G WavesFM: A Foundation Model for Sensing, Communication, and Localization—0
HSACNet: Hierarchical Scale-Aware Consistency Regularized Semi-Supervised Change Detection—0
Parameter-Efficient Continual Fine-Tuning: A Survey—0
Integrating Structural and Semantic Signals in Text-Attributed Graphs with BiGTexCode0
You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models—0
A Decade of Wheat Mapping for Lebanon—0
Balancing Stability and Plasticity in Pretrained Detector: A Dual-Path Framework for Incremental Object Detection—0
CROSSAN: Towards Efficient and Effective Adaptation of Multiple Multimodal Foundation Models for Sequential RecommendationCode0
Enhancing knowledge retention for continual learning with domain-specific adapters and features gating—0
Teaching pathology foundation models to accurately predict gene expression with parameter efficient knowledge transfer—0
AROMA: Autonomous Rank-one Matrix AdaptationCode0
FISH-Tuning: Enhancing PEFT Methods with Fisher Information—0
Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation—0
CLIP-SLA: Parameter-Efficient CLIP Adaptation for Continuous Sign Language RecognitionCode0
DynMoLE: Boosting Mixture of LoRA Experts Fine-Tuning with a Hybrid Routing MechanismCode0
Generalized Tensor-based Parameter-Efficient Fine-Tuning via Lie Group Transformations—0
MetaLoRA: Tensor-Enhanced Adaptive Low-Rank Fine-tuning—0
Mixture of Routers—0
Efficient Adaptation For Remote Sensing Visual Grounding—0
AutoPsyC: Automatic Recognition of Psychodynamic Conflicts from Semi-structured Interviews with Large Language Models—0
RocketPPA: Code-Level Power, Performance, and Area Prediction via LLM and Mixture of Experts—0
MSPLoRA: A Multi-Scale Pyramid Low-Rank Adaptation for Efficient Model Fine-TuningCode0
Enhancing Multi-modal Models with Heterogeneous MoE Adapters for Fine-tuning—0
IAP: Improving Continual Learning of Vision-Language Models via Instance-Aware PromptingCode0
Explainable ICD Coding via Entity Linking—0
QUAD: Quantization and Parameter-Efficient Tuning of LLM with Activation DecompositionCode0
Efficient Continual Adaptation of Pretrained Robotic Policy with Online Meta-Learned Adapters—0
Hiding Images in Diffusion Models by Editing Learned Score FunctionsCode0
VTD-CLIP: Video-to-Text Discretization via Prompting CLIPCode0
Coeff-Tuning: A Graph Filter Subspace View for Tuning Attention-Based Large ModelsCode0
Visual Variational Autoencoder Prompt Tuning—0
TRACE: Time SeRies PArameter EffiCient FinE-tuning—0
PE-CLIP: A Parameter-Efficient Fine-Tuning of Vision Language Models for Dynamic Facial Expression RecognitionCode0
VP-NTK: Exploring the Benefits of Visual Prompting in Differentially Private Data Synthesis—0
FedSCA: Federated Tuning with Similarity-guided Collaborative Aggregation for Heterogeneous Medical Image Segmentation—0
MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts—0
Quantum-Enhanced LLM Efficient Fine Tuning—0
Watch and Learn: Leveraging Expert Knowledge and Language for Surgical Video Understanding—0
MoLEx: Mixture of Layer Experts for Finetuning with Sparse UpcyclingCode0
Enhancing Aviation Communication Transcription: Fine-Tuning Distil-Whisper with LoRA—0
Efficient Federated Fine-Tuning of Large Language Models with Layer Dropout—0
Singular Value Fine-tuning for Few-Shot Class-Incremental Learning—0
Enhanced Continual Learning of Vision-Language Models with Model Fusion—0
Project-Probe-Aggregate: Efficient Fine-Tuning for Group Robustness—0
Privacy-Preserved Automated Scoring using Federated Learning for Educational ResearchCode0
MoFE: Mixture of Frozen Experts Architecture—0
Lifelong Learning with Task-Specific Adaptation: Addressing the Stability-Plasticity Dilemma—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