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 451–475 of 935 papers

TitleStatusHype
Low-Rank Adapters Meet Neural Architecture Search for LLM Compression—0
Is your LLM trapped in a Mental Set? Investigative study on how mental sets affect the reasoning capabilities of LLMs—0
EDoRA: Efficient Weight-Decomposed Low-Rank Adaptation via Singular Value DecompositionCode0
OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning—0
Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Models—0
LeMo: Enabling LEss Token Involvement for MOre Context Fine-tuning—0
A Multi-Encoder Frozen-Decoder Approach for Fine-Tuning Large Language Models—0
TriAdaptLoRA: Brain-Inspired Triangular Adaptive Low-Rank Adaptation for Parameter-Efficient Fine-Tuning—0
Optimizing Language Models for Grammatical Acceptability: A Comparative Study of Fine-Tuning Techniques—0
A Hessian-informed hyperparameter optimization for differential learning rate—0
Speech Recognition for Automatically Assessing Afrikaans and isiXhosa Preschool Oral Narratives—0
How to Tune a Multilingual Encoder Model for Germanic Languages: A Study of PEFT, Full Fine-Tuning, and Language AdaptersCode0
A Text-Based Knowledge-Embedded Soft Sensing Modeling Approach for General Industrial Process Tasks Based on Large Language Model—0
TADFormer : Task-Adaptive Dynamic Transformer for Efficient Multi-Task Learning—0
MedFocusCLIP : Improving few shot classification in medical datasets using pixel wise attention—0
Spectral-Aware Low-Rank Adaptation for Speaker VerificationCode0
ADePT: Adaptive Decomposed Prompt Tuning for Parameter-Efficient Fine-tuningCode0
Efficient Deployment of Large Language Models on Resource-constrained Devices—0
tCURLoRA: Tensor CUR Decomposition Based Low-Rank Parameter Adaptation and Its Application in Medical Image Segmentation—0
SaLoRA: Safety-Alignment Preserved Low-Rank Adaptation—0
SoMA: Singular Value Decomposed Minor Components Adaptation for Domain Generalizable Representation Learning—0
Keep the Balance: A Parameter-Efficient Symmetrical Framework for RGB+X Semantic Segmentation—0
pFedMxF: Personalized Federated Class-Incremental Learning with Mixture of Frequency Aggregation—0
BiLoRA: Almost-Orthogonal Parameter Spaces for Continual Learning—0
LoKi: Low-dimensional KAN for Efficient Fine-tuning Image Models—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