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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 501–550 of 935 papers

TitleStatusHype
MoDE: Effective Multi-task Parameter Efficient Fine-Tuning with a Mixture of Dyadic Experts—0
Leveraging Parameter Efficient Training Methods for Low Resource Text Classification: A Case Study in Marathi—0
SARA: Singular-Value Based Adaptive Low-Rank Adaption—0
Memory-Efficient Orthogonal Fine-Tuning with Principal Subspace Adaptation—0
6G WavesFM: A Foundation Model for Sensing, Communication, and Localization—0
A Comprehensive Evaluation of Large Language Models on Aspect-Based Sentiment Analysis—0
Activation Control for Efficiently Eliciting Long Chain-of-thought Ability of Language Models—0
AdaFish: Fast low-rank parameter-efficient fine-tuning by using second-order information—0
Adapter-Based Extension of Multi-Speaker Text-to-Speech Model for New Speakers—0
Adapter-X: A Novel General Parameter-Efficient Fine-Tuning Framework for Vision—0
Adapting Segment Anything Model to Melanoma Segmentation in Microscopy Slide Images—0
Adaptive Layer Selection for Efficient Vision Transformer Fine-Tuning—0
Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices—0
Adaptive parameter-efficient fine-tuning via Hessian-informed subset selection—0
Adaptive Rank Allocation for Federated Parameter-Efficient Fine-Tuning of Language Models—0
AdaViPro: Region-based Adaptive Visual Prompt for Large-Scale Models Adapting—0
A Decade of Wheat Mapping for Lebanon—0
AdUE: Improving uncertainty estimation head for LoRA adapters in LLMs—0
Advancing Enterprise Spatio-Temporal Forecasting Applications: Data Mining Meets Instruction Tuning of Language Models For Multi-modal Time Series Analysis in Low-Resource Settings—0
A Fine-tuning Enhanced RAG System with Quantized Influence Measure as AI Judge—0
AFLoRA: Adaptive Freezing of Low Rank Adaptation in Parameter Efficient Fine-Tuning of Large Models—0
A GEN AI Framework for Medical Note Generation—0
Ahead-of-Time P-Tuning—0
A Hessian-informed hyperparameter optimization for differential learning rate—0
Aligner: One Global Token is Worth Millions of Parameters When Aligning Large Language Models—0
ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models—0
A LoRA is Worth a Thousand Pictures—0
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models—0
AMR Parsing with Instruction Fine-tuned Pre-trained Language Models—0
A Multi-Encoder Frozen-Decoder Approach for Fine-Tuning Large Language Models—0
An Empirical Study of Parameter Efficient Fine-tuning on Vision-Language Pre-train Model—0
An Empirical Study on the Transferability of Transformer Modules in Parameter-Efficient Fine-Tuning—0
A New Chinese Landscape Paintings Generation Model based on Stable Diffusion using DreamBooth—0
An Improved Empirical Fisher Approximation for Natural Gradient Descent—0
A Novel Hybrid Parameter-Efficient Fine-Tuning Approach for Hippocampus Segmentation and Alzheimer's Disease Diagnosis—0
A Parameter-efficient Language Extension Framework for Multilingual ASR—0
A Rank Stabilization Scaling Factor for Fine-Tuning with LoRA—0
ARD-LoRA: Dynamic Rank Allocation for Parameter-Efficient Fine-Tuning of Foundation Models with Heterogeneous Adaptation Needs—0
A Single Linear Layer Yields Task-Adapted Low-Rank Matrices—0
ASLoRA: Adaptive Sharing Low-Rank Adaptation Across Layers—0
A Split-and-Privatize Framework for Large Language Model Fine-Tuning—0
Assessing Translation capabilities of Large Language Models involving English and Indian Languages—0
Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection—0
A Survey of Recent Backdoor Attacks and Defenses in Large Language Models—0
A Survey on Efficient Federated Learning Methods for Foundation Model Training—0
A Text-Based Knowledge-Embedded Soft Sensing Modeling Approach for General Industrial Process Tasks Based on Large Language Model—0
AuroRA: Breaking Low-Rank Bottleneck of LoRA with Nonlinear Mapping—0
Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models—0
AutoPsyC: Automatic Recognition of Psychodynamic Conflicts from Semi-structured Interviews with Large Language Models—0
Balancing Stability and Plasticity in Pretrained Detector: A Dual-Path Framework for Incremental Object Detection—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