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

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Showing 601–625 of 935 papers

TitleStatusHype
Cross-Lingual Speech Emotion Recognition: Humans vs. Self-Supervised ModelsCode0
Block Expanded DINORET: Adapting Natural Domain Foundation Models for Retinal Imaging Without Catastrophic Forgetting—0
Enabling Efficient On-Device Fine-Tuning of LLMs Using Only Inference Engines—0
Flat-LoRA: Low-Rank Adaption over a Flat Loss Landscape—0
Obliviate: Neutralizing Task-agnostic Backdoors within the Parameter-efficient Fine-tuning ParadigmCode0
HUT: A More Computation Efficient Fine-Tuning Method With Hadamard Updated Transformation—0
Beyond LoRA: Exploring Efficient Fine-Tuning Techniques for Time Series Foundational Models—0
LPT++: Efficient Training on Mixture of Long-tailed Experts—0
THaMES: An End-to-End Tool for Hallucination Mitigation and Evaluation in Large Language ModelsCode0
From Text to Emoji: How PEFT-Driven Personality Manipulation Unleashes the Emoji Potential in LLMs—0
COMFORT: A Continual Fine-Tuning Framework for Foundation Models Targeted at Consumer Healthcare—0
Risks When Sharing LoRA Fine-Tuned Diffusion Model Weights—0
SVFit: Parameter-Efficient Fine-Tuning of Large Pre-Trained Models Using Singular Values—0
iConFormer: Dynamic Parameter-Efficient Tuning with Input-Conditioned Adaptation—0
Robust Federated Finetuning of Foundation Models via Alternating Minimization of LoRA—0
Deconfounded Causality-aware Parameter-Efficient Fine-Tuning for Problem-Solving Improvement of LLMs—0
User-Specific Dialogue Generation with User Profile-Aware Pre-Training Model and Parameter-Efficient Fine-Tuning—0
A Novel Hybrid Parameter-Efficient Fine-Tuning Approach for Hippocampus Segmentation and Alzheimer's Disease Diagnosis—0
Scaling Up Summarization: Leveraging Large Language Models for Long Text Extractive Summarization—0
FedMCP: Parameter-Efficient Federated Learning with Model-Contrastive Personalization—0
GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMsCode0
Pre-training Everywhere: Parameter-Efficient Fine-Tuning for Medical Image Analysis via Target Parameter Pre-training—0
StyleSpeech: Parameter-efficient Fine Tuning for Pre-trained Controllable Text-to-SpeechCode0
Step-by-Step Unmasking for Parameter-Efficient Fine-tuning of Large Language ModelsCode0
Question answering system of bridge design specification based on large language modelCode0
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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