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 501–525 of 935 papers

TitleStatusHype
PETALface: Parameter Efficient Transfer Learning for Low-resolution Face Recognition—0
CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices—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
QueEn: A Large Language Model for Quechua-English Translation—0
PETapter: Leveraging PET-style classification heads for modular few-shot parameter-efficient fine-tuning—0
Streaming Detection of Queried Event StartCode0
Mixture of Physical Priors Adapter for Parameter-Efficient Fine-Tuning—0
CPP-UT-Bench: Can LLMs Write Complex Unit Tests in C++?—0
A Comprehensive Evaluation of Large Language Models on Aspect-Based Sentiment Analysis—0
LoRA Diffusion: Zero-Shot LoRA Synthesis for Diffusion Model Personalization—0
Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based AdaptationCode0
PEFT-as-an-Attack! Jailbreaking Language Models during Federated Parameter-Efficient Fine-Tuning—0
DESIRE: Dynamic Knowledge Consolidation for Rehearsal-Free Continual Learning—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
Promptable Anomaly Segmentation with SAM Through Self-Perception Tuning—0
Towards Efficient Model-Heterogeneity Federated Learning for Large Models—0
Graph Adapter of EEG Foundation Models for Parameter Efficient Fine Tuning—0
Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models—0
LoRA-Mini : Adaptation Matrices Decomposition and Selective Training—0
LoRA-FAIR: Federated LoRA Fine-Tuning with Aggregation and Initialization Refinement—0
Parameter Efficient Mamba Tuning via Projector-targeted Diagonal-centric Linear Transformation—0
Visual Cue Enhancement and Dual Low-Rank Adaptation for Efficient Visual Instruction Fine-Tuning—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