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 901–935 of 935 papers

TitleStatusHype
Systematic Analysis for Pretrained Language Model Priming for Parameter-Efficient Fine-tuning—0
On the Effectiveness of Parameter-Efficient Fine-TuningCode1
HyperTuning: Toward Adapting Large Language Models without Back-propagation—0
Cross-Modal Adapter for Text-Video RetrievalCode1
Multi-Head Adapter Routing for Cross-Task Generalization—0
Resource-Efficient Transfer Learning From Speech Foundation Model Using Hierarchical Feature Fusion—0
Adapter-Based Extension of Multi-Speaker Text-to-Speech Model for New Speakers—0
AdaMix: Mixture-of-Adaptations for Parameter-efficient Model TuningCode1
Prompting through Prototype: A Prototype-based Prompt Learning on Pretrained Vision-Language Models—0
Scaling & Shifting Your Features: A New Baseline for Efficient Model TuningCode1
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models—0
Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks—0
Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning—0
Efficient Few-Shot Learning Without PromptsCode4
Meta-Adapters: Parameter Efficient Few-shot Fine-tuning through Meta-LearningCode0
ST-Adapter: Parameter-Efficient Image-to-Video Transfer LearningCode1
Know Where You're Going: Meta-Learning for Parameter-Efficient Fine-Tuning—0
AdaMix: Mixture-of-Adaptations for Parameter-efficient Model TuningCode1
When does Parameter-Efficient Transfer Learning Work for Machine Translation?Code0
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context LearningCode4
Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-TuningCode1
Hyperdecoders: Instance-specific decoders for multi-task NLPCode1
CLIP Models are Few-shot Learners: Empirical Studies on VQA and Visual Entailment—0
HyperPELT: Unified Parameter-Efficient Language Model Tuning for Both Language and Vision-and-Language Tasks—0
Meta-Adapter: Parameter Efficient Few-Shot Learning through Meta-Learning—0
DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language ModelsCode0
BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models—0
LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot LearnersCode1
Towards a Unified View of Parameter-Efficient Transfer LearningCode1
Efficient Test Time Adapter Ensembling for Low-resource Language VarietiesCode1
BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-modelsCode1
LoRA: Low-Rank Adaptation of Large Language ModelsCode2
Compacter: Efficient Low-Rank Hypercomplex Adapter LayersCode3
Parameter-efficient Multi-task Fine-tuning for Transformers via Shared HypernetworksCode1
Style Attuned Pre-training and Parameter Efficient Fine-tuning for Spoken Language Understanding—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