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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

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Showing 226–250 of 935 papers

TitleStatusHype
Low-Rank Adapters Meet Neural Architecture Search for LLM Compression—0
Parameter-Efficient Fine-Tuning for Foundation ModelsCode2
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
LeMo: Enabling LEss Token Involvement for MOre Context Fine-tuning—0
Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image 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 Multi-Encoder Frozen-Decoder Approach for Fine-Tuning Large Language Models—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
Spectral-Aware Low-Rank Adaptation for Speaker VerificationCode0
MedFocusCLIP : Improving few shot classification in medical datasets using pixel wise attention—0
ADePT: Adaptive Decomposed Prompt Tuning for Parameter-Efficient Fine-tuningCode0
Efficient Deployment of Large Language Models on Resource-constrained Devices—0
HALO: Hadamard-Assisted Lower-Precision Optimization for LLMsCode1
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
Rethinking Token Reduction with Parameter-Efficient Fine-Tuning in ViT for Pixel-Level TasksCode0
F^3OCUS - Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics—0
BiLoRA: Almost-Orthogonal Parameter Spaces for Continual Learning—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