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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 576–600 of 935 papers

TitleStatusHype
Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity—0
Parameter Efficient Fine Tuning for Multi-scanner PET to PET Reconstruction—0
Parameter-efficient Fine-tuning in Hyperspherical Space for Open-vocabulary Semantic Segmentation—0
Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies—0
Parameter-Efficient Fine-Tuning Medical Multimodal Large Language Models for Medical Visual Grounding—0
Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment—0
Parameter-Efficient Fine-Tuning of Multispectral Foundation Models for Hyperspectral Image Classification—0
Parameter-Efficient Fine-Tuning of Large Language Models using Semantic Knowledge Tuning—0
Parameter-Efficient Fine-Tuning of Large Language Models for Unit Test Generation: An Empirical Study—0
Parameter-Efficient Fine-Tuning via Circular Convolution—0
Parameter Efficient Fine-tuning via Cross Block Orchestration for Segment Anything Model—0
Parameter-Efficient Fine-Tuning via Selective Discrete Cosine Transform—0
Parameter-Efficient Fine-Tuning With Adapters—0
Parameter-Efficient Fine-Tuning with Column Space Projection—0
Parameter Efficient Mamba Tuning via Projector-targeted Diagonal-centric Linear Transformation—0
Low-Rank Adaptation for Multilingual Summarization: An Empirical Study—0
Parameter-Efficient Tuning Large Language Models for Graph Representation Learning—0
Parameterizing Context: Unleashing the Power of Parameter-Efficient Fine-Tuning and In-Context Tuning for Continual Table Semantic Parsing—0
Partial Fine-Tuning: A Successor to Full Fine-Tuning for Vision Transformers—0
PC-LoRA: Low-Rank Adaptation for Progressive Model Compression with Knowledge Distillation—0
PEDRO: Parameter-Efficient Fine-tuning with Prompt DEpenDent Representation MOdification—0
PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models—0
PEFT-as-an-Attack! Jailbreaking Language Models during Federated Parameter-Efficient Fine-Tuning—0
PEFTDebias : Capturing debiasing information using PEFTs—0
PEFT-MedAware: Large Language Model for Medical Awareness—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