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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 551–600 of 935 papers

TitleStatusHype
LoRA as a Flexible Framework for Securing Large Vision Systems—0
LoRACode: LoRA Adapters for Code Embeddings—0
LoRA Diffusion: Zero-Shot LoRA Synthesis for Diffusion Model Personalization—0
LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation—0
LoRA Dropout as a Sparsity Regularizer for Overfitting Control—0
LoRA ensembles for large language model fine-tuning—0
LoRA-FAIR: Federated LoRA Fine-Tuning with Aggregation and Initialization Refinement—0
LoRAGuard: An Effective Black-box Watermarking Approach for LoRAs—0
LoRA-Mini : Adaptation Matrices Decomposition and Selective Training—0
LoRA-X: Bridging Foundation Models with Training-Free Cross-Model Adaptation—0
LORD: Low Rank Decomposition Of Monolingual Code LLMs For One-Shot Compression—0
LORENZA: Enhancing Generalization in Low-Rank Gradient LLM Training via Efficient Zeroth-Order Adaptive SAM—0
LoRTA: Low Rank Tensor Adaptation of Large Language Models—0
LoTR: Low Tensor Rank Weight Adaptation—0
LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits—0
Low-Rank Adaptation of Neural Fields—0
Low-Rank Adapters Meet Neural Architecture Search for LLM Compression—0
Low-rank Attention Side-Tuning for Parameter-Efficient Fine-Tuning—0
LPT++: Efficient Training on Mixture of Long-tailed Experts—0
Parameter-Efficient Continual Fine-Tuning: A Survey—0
Parameter Efficient Continual Learning with Dynamic Low-Rank Adaptation—0
Parameter Efficient Fine Tuning: A Comprehensive Analysis Across Applications—0
Parameter-Efficient Fine-Tuning Design Spaces—0
Parameter-Efficient Fine-Tuning for Continual Learning: A Neural Tangent Kernel Perspective—0
Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey—0
Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity—0
Parameter Efficient Fine Tuning for Multi-scanner PET to PET Reconstruction—0
Parameter-efficient Fine-tuning in Hyperspherical Space for Open-vocabulary Semantic Segmentation—0
Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies—0
Parameter-Efficient Fine-Tuning Medical Multimodal Large Language Models for Medical Visual Grounding—0
Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment—0
Parameter-Efficient Fine-Tuning of Multispectral Foundation Models for Hyperspectral Image Classification—0
Parameter-Efficient Fine-Tuning of Large Language Models using Semantic Knowledge Tuning—0
Parameter-Efficient Fine-Tuning of Large Language Models for Unit Test Generation: An Empirical Study—0
Parameter-Efficient Fine-Tuning via Circular Convolution—0
Parameter Efficient Fine-tuning via Cross Block Orchestration for Segment Anything Model—0
Parameter-Efficient Fine-Tuning via Selective Discrete Cosine Transform—0
Parameter-Efficient Fine-Tuning With Adapters—0
Parameter-Efficient Fine-Tuning with Column Space Projection—0
Parameter Efficient Mamba Tuning via Projector-targeted Diagonal-centric Linear Transformation—0
Low-Rank Adaptation for Multilingual Summarization: An Empirical Study—0
Parameter-Efficient Tuning Large Language Models for Graph Representation Learning—0
Parameterizing Context: Unleashing the Power of Parameter-Efficient Fine-Tuning and In-Context Tuning for Continual Table Semantic Parsing—0
Partial Fine-Tuning: A Successor to Full Fine-Tuning for Vision Transformers—0
PC-LoRA: Low-Rank Adaptation for Progressive Model Compression with Knowledge Distillation—0
PEDRO: Parameter-Efficient Fine-tuning with Prompt DEpenDent Representation MOdification—0
PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models—0
PEFT-as-an-Attack! Jailbreaking Language Models during Federated Parameter-Efficient Fine-Tuning—0
PEFTDebias : Capturing debiasing information using PEFTs—0
PEFT-MedAware: Large Language Model for Medical Awareness—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