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

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Showing 51–100 of 935 papers

TitleStatusHype
CLaDMoP: Learning Transferrable Models from Successful Clinical Trials via LLMsCode0
HD-PiSSA: High-Rank Distributed Orthogonal Adaptation—0
KerZOO: Kernel Function Informed Zeroth-Order Optimization for Accurate and Accelerated LLM Fine-Tuning—0
Activation Control for Efficiently Eliciting Long Chain-of-thought Ability of Language Models—0
Explain Less, Understand More: Jargon Detection via Personalized Parameter-Efficient Fine-tuning—0
Representation Discrepancy Bridging Method for Remote Sensing Image-Text Retrieval—0
15,500 Seconds: Lean UAV Classification Leveraging PEFT and Pre-Trained NetworksCode0
4,500 Seconds: Small Data Training Approaches for Deep UAV Audio ClassificationCode0
AdUE: Improving uncertainty estimation head for LoRA adapters in LLMs—0
CoLA: Collaborative Low-Rank AdaptationCode0
Few-Shot Adversarial Low-Rank Fine-Tuning of Vision-Language Models—0
Gated Integration of Low-Rank Adaptation for Continual Learning of Language ModelsCode1
Parameter-Efficient Fine-Tuning of Multispectral Foundation Models for Hyperspectral Image Classification—0
VP Lab: a PEFT-Enabled Visual Prompting Laboratory for Semantic Segmentation—0
Quaff: Quantized Parameter-Efficient Fine-Tuning under Outlier Spatial Stability HypothesisCode1
Privacy Preserving Conversion Modeling in Data Clean Room—0
OSoRA: Output-Dimension and Singular-Value Initialized Low-Rank Adaptation—0
ABBA: Highly Expressive Hadamard Product Adaptation for Large Language ModelsCode1
Dual Decomposition of Weights and Singular Value Low Rank Adaptation—0
Efficient Federated Class-Incremental Learning of Pre-Trained Models via Task-agnostic Low-rank Residual Adaptation—0
Adaptive parameter-efficient fine-tuning via Hessian-informed subset selection—0
SRLoRA: Subspace Recomposition in Low-Rank Adaptation via Importance-Based Fusion and Reinitialization—0
Exploring Sparsity for Parameter Efficient Fine Tuning Using WaveletsCode0
Parameter Efficient Continual Learning with Dynamic Low-Rank Adaptation—0
Memory-Efficient Orthogonal Fine-Tuning with Principal Subspace Adaptation—0
Reasoning on a Budget: Miniaturizing DeepSeek R1 with SFT-GRPO Alignment for Instruction-Tuned LLMsCode1
Multi-Token Prediction Needs RegistersCode1
PT-MoE: An Efficient Finetuning Framework for Integrating Mixture-of-Experts into Prompt TuningCode0
Parameter-Efficient Fine-Tuning of Vision Foundation Model for Forest Floor Segmentation from UAV ImageryCode0
DAPE: Dual-Stage Parameter-Efficient Fine-Tuning for Consistent Video Editing with Diffusion Models—0
Efficient Telecom Specific LLM: TSLAM-Mini with QLoRA and Digital Twin Data—0
Enfoque Odychess: Un método dialéctico, constructivista y adaptativo para la enseñanza del ajedrez con inteligencias artificiales generativas—0
Leveraging Large Language Models for enzymatic reaction prediction and characterizationCode0
Vision Graph Prompting via Semantic Low-Rank DecompositionCode1
On-Device LLM for Context-Aware Wi-Fi RoamingCode0
GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision ModelCode1
Deepfakes on Demand: the rise of accessible non-consensual deepfake image generatorsCode0
HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models—0
SpectrumFM: A Foundation Model for Intelligent Spectrum ManagementCode1
Federated Adapter on Foundation Models: An Out-Of-Distribution Approach—0
AdCare-VLM: Leveraging Large Vision Language Model (LVLM) to Monitor Long-Term Medication Adherence and CareCode0
Vision-Language Model-Based Semantic-Guided Imaging Biomarker for Early Lung Cancer Detection—0
DeeCLIP: A Robust and Generalizable Transformer-Based Framework for Detecting AI-Generated ImagesCode1
NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation—0
Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging—0
Prompt-Tuning SAM: From Generalist to Specialist with only 2048 Parameters and 16 Training Images—0
PointLoRA: Low-Rank Adaptation with Token Selection for Point Cloud LearningCode1
CLIP-IT: CLIP-based Pairing for Histology Images ClassificationCode0
Low-Rank Adaptation of Neural Fields—0
SOLIDO: A Robust Watermarking Method for Speech Synthesis via Low-Rank Adaptation—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