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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 151–200 of 935 papers

TitleStatusHype
IntLoRA: Integral Low-rank Adaptation of Quantized Diffusion ModelsCode1
Hyperdecoders: Instance-specific decoders for multi-task NLPCode1
IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFTCode1
CoPEFT: Fast Adaptation Framework for Multi-Agent Collaborative Perception with Parameter-Efficient Fine-TuningCode1
Embedded Prompt Tuning: Towards Enhanced Calibration of Pretrained Models for Medical ImagesCode1
Imaging foundation model for universal enhancement of non-ideal measurement CTCode1
Hydra: Multi-head Low-rank Adaptation for Parameter Efficient Fine-tuningCode1
IncreLoRA: Incremental Parameter Allocation Method for Parameter-Efficient Fine-tuningCode1
ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot FillerCode1
Efficient Test Time Adapter Ensembling for Low-resource Language VarietiesCode1
Joint Localization and Activation Editing for Low-Resource Fine-TuningCode1
Customizing Language Models with Instance-wise LoRA for Sequential RecommendationCode1
Harnessing Large Language Models for Text-Rich Sequential RecommendationCode1
CVPT: Cross-Attention help Visual Prompt Tuning adapt visual taskCode1
HALO: Hadamard-Assisted Lower-Precision Optimization for LLMsCode1
HiFT: A Hierarchical Full Parameter Fine-Tuning StrategyCode1
Content-based Controls For Music Large Language ModelingCode1
Efficient Fine-tuning of Audio Spectrogram Transformers via Soft Mixture of AdaptersCode1
Gradient-based Parameter Selection for Efficient Fine-TuningCode1
Density Adaptive Attention is All You Need: Robust Parameter-Efficient Fine-Tuning Across Multiple ModalitiesCode1
ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and QuantizationCode1
Generative Parameter-Efficient Fine-TuningCode1
A Comprehensive Analysis of Adapter EfficiencyCode1
Gated Integration of Low-Rank Adaptation for Continual Learning of Language ModelsCode1
GIST: Improving Parameter Efficient Fine Tuning via Knowledge InteractionCode1
Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuningCode1
KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language ModelsCode1
FLoRA: Low-Rank Core Space for N-dimensionCode1
Low-Rank Rescaled Vision Transformer Fine-Tuning: A Residual Design ApproachCode1
FonTS: Text Rendering with Typography and Style ControlsCode1
AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-TuningCode1
DiffFit: Unlocking Transferability of Large Diffusion Models via Simple Parameter-Efficient Fine-TuningCode1
AdaMix: Mixture-of-Adaptations for Parameter-efficient Model TuningCode1
Efficient Self-Supervised Adaptation for Medical Image AnalysisCode1
AutoVP: An Automated Visual Prompting Framework and BenchmarkCode1
MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image SegmentationCode1
APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and InferenceCode1
FineDiffusion: Scaling up Diffusion Models for Fine-grained Image Generation with 10,000 ClassesCode1
Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA RegionCode1
MeteoRA: Multiple-tasks Embedded LoRA for Large Language ModelsCode1
Forecast-PEFT: Parameter-Efficient Fine-Tuning for Pre-trained Motion Forecasting ModelsCode1
A Prompt Learning Framework for Source Code SummarizationCode1
MoRe Fine-Tuning with 10x Fewer ParametersCode1
MoST: Efficient Monarch Sparse Tuning for 3D Representation LearningCode1
TS-SAM: Fine-Tuning Segment-Anything Model for Downstream TasksCode1
Do Vision Foundation Models Enhance Domain Generalization in Medical Image Segmentation?Code1
FedJudge: Federated Legal Large Language ModelCode1
DropBP: Accelerating Fine-Tuning of Large Language Models by Dropping Backward PropagationCode1
AdaMix: Mixture-of-Adaptations for Parameter-efficient Model TuningCode1
Ferret: Federated Full-Parameter Tuning at Scale for Large Language ModelsCode1
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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