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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 451–500 of 935 papers

TitleStatusHype
Pluto and Charon: A Time and Memory Efficient Collaborative Edge AI Framework for Personal LLMs Fine-Tuning—0
V-RoAst: Visual Road Assessment. Can VLM be a Road Safety Assessor Using the iRAP Standard?Code1
Customizing Language Models with Instance-wise LoRA for Sequential RecommendationCode1
TeamLoRA: Boosting Low-Rank Adaptation with Expert Collaboration and CompetitionCode0
NoRA: Nested Low-Rank Adaptation for Efficient Fine-Tuning Large Models—0
Combo: Co-speech holistic 3D human motion generation and efficient customizable adaptation in harmony—0
MergeRepair: An Exploratory Study on Merging Task-Specific Adapters in Code LLMs for Automated Program Repair—0
A New Chinese Landscape Paintings Generation Model based on Stable Diffusion using DreamBooth—0
Learning to Route for Dynamic Adapter Composition in Continual Learning with Language Models—0
SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image SegmentationCode3
Adaptive Layer Selection for Efficient Vision Transformer Fine-Tuning—0
LLMI3D: Empowering LLM with 3D Perception from a Single 2D Image—0
KIND: Knowledge Integration and Diversion for Training Decomposable ModelsCode0
Orchid2024: A cultivar-level dataset and methodology for fine-grained classification of Chinese Cymbidium OrchidsCode0
KIF: Knowledge Identification and Fusion for Language Model Continual LearningCode1
BA-LoRA: Bias-Alleviating Low-Rank Adaptation to Mitigate Catastrophic Inheritance in Large Language ModelsCode0
From Words to Worth: Newborn Article Impact Prediction with LLM—0
FastEdit: Fast Text-Guided Single-Image Editing via Semantic-Aware Diffusion Fine-Tuning—0
SARA: Singular-Value Based Adaptive Low-Rank Adaption—0
Leveraging Parameter Efficient Training Methods for Low Resource Text Classification: A Case Study in Marathi—0
TS-SAM: Fine-Tuning Segment-Anything Model for Downstream TasksCode1
Tensor Train Low-rank Approximation (TT-LoRA): Democratizing AI with Accelerated LLMs—0
MoDE: Effective Multi-task Parameter Efficient Fine-Tuning with a Mixture of Dyadic Experts—0
Correcting Negative Bias in Large Language Models through Negative Attention Score Alignment—0
ELP-Adapters: Parameter Efficient Adapter Tuning for Various Speech Processing Tasks—0
Forecast-PEFT: Parameter-Efficient Fine-Tuning for Pre-trained Motion Forecasting ModelsCode1
Parameter-Efficient Fine-Tuning via Circular Convolution—0
PEFT-U: Parameter-Efficient Fine-Tuning for User PersonalizationCode0
LoRA-Pro: Are Low-Rank Adapters Properly Optimized?Code2
Parameter-Efficient Fine-Tuning for Continual Learning: A Neural Tangent Kernel Perspective—0
Zero-Shot Embeddings Inform Learning and Forgetting with Vision-Language Encoders—0
Learn to Preserve and Diversify: Parameter-Efficient Group with Orthogonal Regularization for Domain GeneralizationCode0
Missing Modality Prediction for Unpaired Multimodal Learning via Joint Embedding of Unimodal Models—0
Turning Generative Models Degenerate: The Power of Data Poisoning Attacks—0
InstructAV: Instruction Fine-tuning Large Language Models for Authorship VerificationCode0
An efficient framework based on large foundation model for cervical cytopathology whole slide image screeningCode0
LoRA-PT: Low-Rank Adapting UNETR for Hippocampus Segmentation Using Principal Tensor Singular Values and VectorsCode0
SDPT: Synchronous Dual Prompt Tuning for Fusion-based Visual-Language Pre-trained ModelsCode0
Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for Medical Image Classification—0
Low-Rank Interconnected Adaptation across LayersCode0
RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation QuantizationCode1
Parameter Efficient Fine Tuning for Multi-scanner PET to PET Reconstruction—0
ROSA: Random Subspace Adaptation for Efficient Fine-TuningCode0
Reprogramming Distillation for Medical Foundation ModelsCode0
A Survey on LoRA of Large Language ModelsCode3
See Further for Parameter Efficient Fine-tuning by Standing on the Shoulders of DecompositionCode2
SBoRA: Low-Rank Adaptation with Regional Weight UpdatesCode0
LoRA-GA: Low-Rank Adaptation with Gradient ApproximationCode3
GPT vs RETRO: Exploring the Intersection of Retrieval and Parameter-Efficient Fine-Tuning—0
ASteISR: Adapting Single Image Super-resolution Pre-trained Model for Efficient Stereo Image Super-resolutionCode0
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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