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 276–300 of 935 papers

TitleStatusHype
A LoRA is Worth a Thousand Pictures—0
ASLoRA: Adaptive Sharing Low-Rank Adaptation Across Layers—0
Adaptive Principal Components Allocation with the _2,g-regularized Gaussian Graphical Model for Efficient Fine-Tuning Large ModelsCode0
CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices—0
PETALface: Parameter Efficient Transfer Learning for Low-resolution Face Recognition—0
BoRA: Bi-dimensional Weight-Decomposed Low-Rank Adaptation—0
Sequential Compression Layers for Efficient Federated Learning in Foundational Models—0
EEG-Based Mental Imagery Task Adaptation via Ensemble of Weight-Decomposed Low-Rank Adapters—0
KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language ModelsCode1
PETapter: Leveraging PET-style classification heads for modular few-shot parameter-efficient fine-tuning—0
QueEn: A Large Language Model for Quechua-English Translation—0
SoRA: Singular Value Decomposed Low-Rank Adaptation for Domain Generalizable Representation LearningCode2
Streaming Detection of Queried Event StartCode0
CPP-UT-Bench: Can LLMs Write Complex Unit Tests in C++?—0
Mixture of Physical Priors Adapter for Parameter-Efficient Fine-Tuning—0
LoRA Diffusion: Zero-Shot LoRA Synthesis for Diffusion Model Personalization—0
A Comprehensive Evaluation of Large Language Models on Aspect-Based Sentiment Analysis—0
Unified Parameter-Efficient Unlearning for LLMsCode1
FonTS: Text Rendering with Typography and Style ControlsCode1
Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based AdaptationCode0
DESIRE: Dynamic Knowledge Consolidation for Rehearsal-Free Continual Learning—0
PEFT-as-an-Attack! Jailbreaking Language Models during Federated Parameter-Efficient Fine-Tuning—0
Challenges in Adapting Multilingual LLMs to Low-Resource Languages using LoRA PEFT Tuning—0
Not All Adapters Matter: Selective Adapter Freezing for Memory-Efficient Fine-Tuning of Language Models—0
Pretrained LLM Adapted with LoRA as a Decision Transformer for Offline RL in Quantitative TradingCode2
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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