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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

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Showing 101–150 of 935 papers

TitleStatusHype
What Lurks Within? Concept Auditing for Shared Diffusion Models at Scale—0
Vision-Centric Representation-Efficient Fine-Tuning for Robust Universal Foreground Segmentation—0
Harnessing Generative LLMs for Enhanced Financial Event Entity Extraction Performance—0
Efficient Federated Split Learning for Large Language Models over Communication Networks—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
CROSSAN: Towards Efficient and Effective Adaptation of Multiple Multimodal Foundation Models for Sequential RecommendationCode0
Balancing Stability and Plasticity in Pretrained Detector: A Dual-Path Framework for Incremental Object Detection—0
Enhancing knowledge retention for continual learning with domain-specific adapters and features gating—0
LoRI: Reducing Cross-Task Interference in Multi-Task Low-Rank AdaptationCode2
Teaching pathology foundation models to accurately predict gene expression with parameter efficient knowledge transfer—0
TASTE: Text-Aligned Speech Tokenization and Embedding for Spoken Language ModelingCode2
Earth-Adapter: Bridge the Geospatial Domain Gaps with Mixture of Frequency AdaptationCode2
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
Generalized Tensor-based Parameter-Efficient Fine-Tuning via Lie Group Transformations—0
MetaLoRA: Tensor-Enhanced Adaptive Low-Rank Fine-tuning—0
DynMoLE: Boosting Mixture of LoRA Experts Fine-Tuning with a Hybrid Routing MechanismCode0
Mixture of Routers—0
Efficient Adaptation For Remote Sensing Visual Grounding—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
AutoPsyC: Automatic Recognition of Psychodynamic Conflicts from Semi-structured Interviews with Large Language Models—0
IAP: Improving Continual Learning of Vision-Language Models via Instance-Aware PromptingCode0
Enhancing Multi-modal Models with Heterogeneous MoE Adapters for Fine-tuning—0
Explainable ICD Coding via Entity Linking—0
QUAD: Quantization and Parameter-Efficient Tuning of LLM with Activation DecompositionCode0
Unlocking the Hidden Potential of CLIP in Generalizable Deepfake DetectionCode2
Hiding Images in Diffusion Models by Editing Learned Score FunctionsCode0
MoST: Efficient Monarch Sparse Tuning for 3D Representation LearningCode1
Efficient Self-Supervised Adaptation for Medical Image AnalysisCode1
VTD-CLIP: Video-to-Text Discretization via Prompting CLIPCode0
SPMTrack: Spatio-Temporal Parameter-Efficient Fine-Tuning with Mixture of Experts for Scalable Visual TrackingCode1
Coeff-Tuning: A Graph Filter Subspace View for Tuning Attention-Based Large ModelsCode0
Efficient Continual Adaptation of Pretrained Robotic Policy with Online Meta-Learned Adapters—0
LoRA Subtraction for Drift-Resistant Space in Exemplar-Free Continual LearningCode1
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
SALT: Singular Value Adaptation with Low-Rank TransformationCode1
Vision-Speech Models: Teaching Speech Models to Converse about ImagesCode3
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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