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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 326–350 of 935 papers

TitleStatusHype
Adaptive parameter-efficient fine-tuning via Hessian-informed subset selection—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
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
Enfoque Odychess: Un método dialéctico, constructivista y adaptativo para la enseñanza del ajedrez con inteligencias artificiales generativas—0
Efficient Telecom Specific LLM: TSLAM-Mini with QLoRA and Digital Twin Data—0
Leveraging Large Language Models for enzymatic reaction prediction and characterizationCode0
On-Device LLM for Context-Aware Wi-Fi RoamingCode0
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
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
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
Low-Rank Adaptation of Neural Fields—0
CLIP-IT: CLIP-based Pairing for Histology Images ClassificationCode0
SOLIDO: A Robust Watermarking Method for Speech Synthesis via Low-Rank Adaptation—0
What Lurks Within? Concept Auditing for Shared Diffusion Models at Scale—0
Vision-Centric Representation-Efficient Fine-Tuning for Robust Universal Foreground Segmentation—0
Efficient Federated Split Learning for Large Language Models over Communication Networks—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