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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
Joint Localization and Activation Editing for Low-Resource Fine-TuningCode1
IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFTCode1
ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot FillerCode1
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
I-MedSAM: Implicit Medical Image Segmentation with Segment AnythingCode1
IncreLoRA: Incremental Parameter Allocation Method for Parameter-Efficient Fine-tuningCode1
EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion ModelsCode1
Imaging foundation model for universal enhancement of non-ideal measurement CTCode1
KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language ModelsCode1
Less Could Be Better: Parameter-efficient Fine-tuning Advances Medical Vision Foundation ModelsCode1
Customizing Language Models with Instance-wise LoRA for Sequential RecommendationCode1
Efficient Fine-tuning of Audio Spectrogram Transformers via Soft Mixture of AdaptersCode1
CVPT: Cross-Attention help Visual Prompt Tuning adapt visual taskCode1
HiFT: A Hierarchical Full Parameter Fine-Tuning StrategyCode1
HALO: Hadamard-Assisted Lower-Precision Optimization for LLMsCode1
Content-based Controls For Music Large Language ModelingCode1
Harnessing Large Language Models for Text-Rich Sequential RecommendationCode1
Hydra: Multi-head Low-rank Adaptation for Parameter Efficient Fine-tuningCode1
GIST: Improving Parameter Efficient Fine Tuning via Knowledge InteractionCode1
ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and QuantizationCode1
A Comprehensive Analysis of Adapter EfficiencyCode1
Generative Parameter-Efficient Fine-TuningCode1
LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical ReportCode1
Gradient-based Parameter Selection for Efficient Fine-TuningCode1
Hyperdecoders: Instance-specific decoders for multi-task NLPCode1
KD-LoRA: A Hybrid Approach to Efficient Fine-Tuning with LoRA and Knowledge DistillationCode1
LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language ModelsCode1
Forecast-PEFT: Parameter-Efficient Fine-Tuning for Pre-trained Motion Forecasting ModelsCode1
FLoRA: Low-Rank Core Space for N-dimensionCode1
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
MasakhaNEWS: News Topic Classification for African languagesCode1
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
LLM-based Medical Assistant Personalization with Short- and Long-Term Memory CoordinationCode1
FonTS: Text Rendering with Typography and Style ControlsCode1
MELoRA: Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-TuningCode1
GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision ModelCode1
A Prompt Learning Framework for Source Code SummarizationCode1
Empirical Study of PEFT techniques for Winter Wheat SegmentationCode1
MoST: Efficient Monarch Sparse Tuning for 3D Representation LearningCode1
Ferret: Federated Full-Parameter Tuning at Scale for Large Language ModelsCode1
Do Vision Foundation Models Enhance Domain Generalization in Medical Image Segmentation?Code1
TS-SAM: Fine-Tuning Segment-Anything Model for Downstream TasksCode1
DropBP: Accelerating Fine-Tuning of Large Language Models by Dropping Backward PropagationCode1
FineDiffusion: Scaling up Diffusion Models for Fine-grained Image Generation with 10,000 ClassesCode1
Gated Integration of Low-Rank Adaptation for Continual Learning of 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