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 326–350 of 935 papers

TitleStatusHype
Parameter-Efficient Fine-Tuning Medical Multimodal Large Language Models for Medical Visual Grounding—0
EF-LLM: Energy Forecasting LLM with AI-assisted Automation, Enhanced Sparse Prediction, Hallucination Detection—0
Efficient Adaptation of Pre-trained Vision Transformer via Householder Transformation—0
MALoRA: Mixture of Asymmetric Low-Rank Adaptation for Enhanced Multi-Task Learning—0
Exploring Gradient Subspaces: Addressing and Overcoming LoRA's Limitations in Federated Fine-Tuning of Large Language Models—0
Capacity Control is an Effective Memorization Mitigation Mechanism in Text-Conditional Diffusion ModelsCode0
Meta-Learning Adaptable Foundation Models—0
IntLoRA: Integral Low-rank Adaptation of Quantized Diffusion ModelsCode1
KD-LoRA: A Hybrid Approach to Efficient Fine-Tuning with LoRA and Knowledge DistillationCode1
Get Large Language Models Ready to Speak: A Late-fusion Approach for Speech Generation—0
Less is More: Extreme Gradient Boost Rank-1 Adaption for Efficient Finetuning of LLMs—0
Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies—0
GeoLoRA: Geometric integration for parameter efficient fine-tuning—0
MiLoRA: Efficient Mixture of Low-Rank Adaptation for Large Language Models Fine-tuning—0
Towards Real Zero-Shot Camouflaged Object Segmentation without Camouflaged AnnotationsCode0
FairLoRA: Unpacking Bias Mitigation in Vision Models with Fairness-Driven Low-Rank Adaptation—0
Benchmarking Pathology Foundation Models: Adaptation Strategies and ScenariosCode0
Natural GaLore: Accelerating GaLore for memory-efficient LLM Training and Fine-tuningCode1
The effect of fine-tuning on language model toxicityCode0
MIRA: A Method of Federated MultI-Task Learning for LaRge LAnguage Models—0
LoRA-IR: Taming Low-Rank Experts for Efficient All-in-One Image RestorationCode2
Unlearning Backdoor Attacks for LLMs with Weak-to-Strong Knowledge DistillationCode0
LoLDU: Low-Rank Adaptation via Lower-Diag-Upper Decomposition for Parameter-Efficient Fine-TuningCode0
Parameter-efficient Adaptation of Multilingual Multimodal Models for Low-resource ASR—0
Unlocking the Capabilities of Masked Generative Models for Image Synthesis via Self-GuidanceCode1
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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