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

Showing 201250 of 935 papers

TitleStatusHype
Generative Parameter-Efficient Fine-TuningCode1
RaDialog: A Large Vision-Language Model for Radiology Report Generation and Conversational AssistanceCode1
I-MedSAM: Implicit Medical Image Segmentation with Segment AnythingCode1
ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and QuantizationCode1
SAMIHS: Adaptation of Segment Anything Model for Intracranial Hemorrhage SegmentationCode1
Aggregate, Decompose, and Fine-Tune: A Simple Yet Effective Factor-Tuning Method for Vision TransformerCode1
The Expressive Power of Low-Rank AdaptationCode1
Content-based Controls For Music Large Language ModelingCode1
Towards a General Framework for Continual Learning with Pre-trainingCode1
When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical ApplicationsCode1
Domain Generalization Using Large Pretrained Models with Mixture-of-AdaptersCode1
AutoVP: An Automated Visual Prompting Framework and BenchmarkCode1
FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image AnalysisCode1
Parameter Efficient Multi-task Model Fusion with Partial LinearizationCode1
EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion ModelsCode1
Point-PEFT: Parameter-Efficient Fine-Tuning for 3D Pre-trained ModelsCode1
Empirical Study of PEFT techniques for Winter Wheat SegmentationCode1
DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion ModelsCode1
MediViSTA: Medical Video Segmentation via Temporal Fusion SAM Adaptation for EchocardiographyCode1
LLM-based Medical Assistant Personalization with Short- and Long-Term Memory CoordinationCode1
MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image SegmentationCode1
FedJudge: Federated Legal Large Language ModelCode1
SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient ChannelsCode1
Hydra: Multi-head Low-rank Adaptation for Parameter Efficient Fine-tuningCode1
DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuningCode1
SoTaNa: The Open-Source Software Development AssistantCode1
IncreLoRA: Incremental Parameter Allocation Method for Parameter-Efficient Fine-tuningCode1
Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language ModelsCode1
Towards Instance-adaptive Inference for Federated LearningCode1
SimTeG: A Frustratingly Simple Approach Improves Textual Graph LearningCode1
Parameter-Efficient Fine-Tuning of LLaMA for the Clinical DomainCode1
OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language ModelsCode1
Make Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-TuningCode1
LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-TuningCode1
MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African LanguagesCode1
Parameter-Efficient Fine-Tuning with Layer Pruning on Free-Text Sequence-to-Sequence ModelingCode1
A Comprehensive Analysis of Adapter EfficiencyCode1
SUR-adapter: Enhancing Text-to-Image Pre-trained Diffusion Models with Large Language ModelsCode1
MasakhaNEWS: News Topic Classification for African languagesCode1
AdapterGNN: Parameter-Efficient Fine-Tuning Improves Generalization in GNNsCode1
DiffFit: Unlocking Transferability of Large Diffusion Models via Simple Parameter-Efficient Fine-TuningCode1
Towards Foundation Models and Few-Shot Parameter-Efficient Fine-Tuning for Volumetric Organ SegmentationCode1
AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-TuningCode1
Sensitivity-Aware Visual Parameter-Efficient Fine-TuningCode1
Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language ModelsCode1
Task-Specific Skill Localization in Fine-tuned Language ModelsCode1
AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-TuningCode1
Towards Practical Plug-and-Play Diffusion ModelsCode1
Visual Query Tuning: Towards Effective Usage of Intermediate Representations for Parameter and Memory Efficient Transfer LearningCode1
On the Effectiveness of Parameter-Efficient Fine-TuningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LLaMA2-7bAccuracy (% )82.63Unverified
2LLaMA2-7bAccuracy (% )82.63Unverified
3LLaMA2-7bAccuracy (% )81.93Unverified
4LLaMA2-7bAccuracy (% )80.28Unverified
#ModelMetricClaimedVerifiedStatus
1LLaMA2-7bAccuracy (% )76.68Unverified
2LLaMA2-7bAccuracy (% )76.67Unverified
3LLaMA2-7bAccuracy (% )76.27Unverified
#ModelMetricClaimedVerifiedStatus
1LLaMA2-7bAccuracy (% )70.8Unverified
2LLaMA2-7bAccuracy (% )70.09Unverified
3LLaMA2-7bAccuracy (% )69.85Unverified