SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 601–625 of 935 papers

TitleStatusHype
LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical ReportCode1
FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition—0
Efficient Remote Sensing with Harmonized Transfer Learning and Modality AlignmentCode2
Parameter-Efficient Tuning Large Language Models for Graph Representation Learning—0
Parameter Efficient Fine-tuning of Self-supervised ViTs without Catastrophic ForgettingCode1
Efficiency in Focus: LayerNorm as a Catalyst for Fine-tuning Medical Visual Language Pre-trained Models—0
Gated Low-rank Adaptation for personalized Code-Switching Automatic Speech Recognition on the low-spec devices—0
Simple, Efficient and Scalable Structure-aware Adapter Boosts Protein Language ModelsCode1
External Prompt Features Enhanced Parameter-efficient Fine-tuning for Salient Object Detection—0
ColA: Collaborative Adaptation with Gradient LearningCode0
Parameter Efficient Fine Tuning: A Comprehensive Analysis Across Applications—0
iTBLS: A Dataset of Interactive Conversations Over Tabular Information—0
TartuNLP @ SIGTYP 2024 Shared Task: Adapting XLM-RoBERTa for Ancient and Historical Languages—0
Mixed Text Recognition with Efficient Parameter Fine-Tuning and Transformer—0
Skeleton: A New Framework for Accelerating Language Models via Task Neuron Localized Prompt Tuning—0
Shears: Unstructured Sparsity with Neural Low-rank Adapter Search—0
Exact and Efficient Unlearning for Large Language Model-based Recommendation—0
LoRA Dropout as a Sparsity Regularizer for Overfitting Control—0
Navigating the Landscape of Large Language Models: A Comprehensive Review and Analysis of Paradigms and Fine-Tuning StrategiesCode0
FLoRA: Enhancing Vision-Language Models with Parameter-Efficient Federated LearningCode0
Any2Point: Empowering Any-modality Large Models for Efficient 3D UnderstandingCode2
PoliTune: Analyzing the Impact of Data Selection and Fine-Tuning on Economic and Political Biases in Large Language ModelsCode0
Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models—0
Using Few-Shot Learning to Classify Primary Lung Cancer and Other Malignancy with Lung Metastasis in Cytological Imaging via Endobronchial Ultrasound Procedures—0
Certified PEFTSmoothing: Parameter-Efficient Fine-Tuning with Randomized Smoothing—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