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 851–900 of 935 papers

TitleStatusHype
Prompt to be Consistent is Better than Self-Consistent? Few-Shot and Zero-Shot Fact Verification with Pre-trained Language ModelsCode0
OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language ModelsCode1
Make Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-TuningCode1
PreQuant: A Task-agnostic Quantization Approach for Pre-trained Language Models—0
Explicit Visual Prompting for Universal Foreground SegmentationsCode2
LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-TuningCode1
Parameter-Efficient Fine-Tuning without Introducing New LatencyCode0
Neural Architecture Search for Parameter-Efficient Fine-tuning of Large Pre-trained Language Models—0
Towards Adaptive Prefix Tuning for Parameter-Efficient Language Model Fine-tuning—0
QLoRA: Efficient Finetuning of Quantized LLMsCode6
Parameter-Efficient Language Model Tuning with Active Learning in Low-Resource SettingsCode0
MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African LanguagesCode1
Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization—0
SPARSEFIT: Few-shot Prompting with Sparse Fine-tuning for Jointly Generating Predictions and Natural Language ExplanationsCode0
Ahead-of-Time P-Tuning—0
G-Adapter: Towards Structure-Aware Parameter-Efficient Transfer Learning for Graph Transformer Networks—0
Parameter-Efficient Fine-Tuning with Layer Pruning on Free-Text Sequence-to-Sequence ModelingCode1
Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity—0
A Comprehensive Analysis of Adapter EfficiencyCode1
Exploring Zero and Few-shot Techniques for Intent Classification—0
LACoS-BLOOM: Low-rank Adaptation with Contrastive objective on 8 bits Siamese-BLOOM—0
Text-guided High-definition Consistency Texture Model—0
SUR-adapter: Enhancing Text-to-Image Pre-trained Diffusion Models with Large Language ModelsCode1
HiFi: High-Information Attention Heads Hold for Parameter-Efficient Model Adaptation—0
Parameter-Efficient Cross-lingual Transfer of Vision and Language Models via Translation-based AlignmentCode0
RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language ModelsCode0
Empirical Analysis of the Strengths and Weaknesses of PEFT Techniques for LLMs—0
AMR Parsing with Instruction Fine-tuned Pre-trained Language Models—0
MasakhaNEWS: News Topic Classification for African languagesCode1
AdapterGNN: Parameter-Efficient Fine-Tuning Improves Generalization in GNNsCode1
DiffFit: Unlocking Transferability of Large Diffusion Models via Simple Parameter-Efficient Fine-TuningCode1
Strong Baselines for Parameter Efficient Few-Shot Fine-tuning—0
GlyphDraw: Seamlessly Rendering Text with Intricate Spatial Structures in Text-to-Image GenerationCode2
Towards Foundation Models and Few-Shot Parameter-Efficient Fine-Tuning for Volumetric Organ SegmentationCode1
Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-TuningCode0
RCA: Region Conditioned Adaptation for Visual Abductive ReasoningCode0
AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-TuningCode1
Sensitivity-Aware Visual Parameter-Efficient Fine-TuningCode1
Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language ModelsCode1
Task-Specific Skill Localization in Fine-tuned Language ModelsCode1
An Empirical Study on the Transferability of Transformer Modules in Parameter-Efficient Fine-Tuning—0
AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-TuningCode1
Parameter-Efficient Fine-Tuning Design Spaces—0
Adapting Shortcut With Normalizing Flow: An Efficient Tuning Framework for Visual RecognitionCode0
Understanding and Improving Transfer Learning of Deep Models via Neural Collapse—0
SPT: Semi-Parametric Prompt Tuning for Multitask Prompted Learning—0
HINT: Hypernetwork Instruction Tuning for Efficient Zero- & Few-Shot Generalisation—0
Towards Practical Plug-and-Play Diffusion ModelsCode1
Parameter-Efficient Finetuning of Transformers for Source CodeCode0
Visual Query Tuning: Towards Effective Usage of Intermediate Representations for Parameter and Memory Efficient Transfer LearningCode1
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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