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 751–775 of 935 papers

TitleStatusHype
LORENZA: Enhancing Generalization in Low-Rank Gradient LLM Training via Efficient Zeroth-Order Adaptive SAM—0
LoRTA: Low Rank Tensor Adaptation of Large Language Models—0
LoTR: Low Tensor Rank Weight Adaptation—0
LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits—0
Low-Rank Adaptation of Neural Fields—0
Low-Rank Adapters Meet Neural Architecture Search for LLM Compression—0
Low-rank Attention Side-Tuning for Parameter-Efficient Fine-Tuning—0
LPT++: Efficient Training on Mixture of Long-tailed Experts—0
LSR-Adapt: Ultra-Efficient Parameter Tuning with Matrix Low Separation Rank Kernel Adaptation—0
MALoRA: Mixture of Asymmetric Low-Rank Adaptation for Enhanced Multi-Task Learning—0
Mamba State-Space Models Are Lyapunov-Stable Learners—0
MAP: Revisiting Weight Decomposition for Low-Rank Adaptation—0
Adapters Mixup: Mixing Parameter-Efficient Adapters to Enhance the Adversarial Robustness of Fine-tuned Pre-trained Text Classifiers—0
MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts—0
Matching Markets Meet LLMs: Algorithmic Reasoning with Ranked Preferences—0
Matrix-Transformation Based Low-Rank Adaptation (MTLoRA): A Brain-Inspired Method for Parameter-Efficient Fine-Tuning—0
MedFocusCLIP : Improving few shot classification in medical datasets using pixel wise attention—0
Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization—0
MergeRepair: An Exploratory Study on Merging Task-Specific Adapters in Code LLMs for Automated Program Repair—0
Meta-Adapter: Parameter Efficient Few-Shot Learning through Meta-Learning—0
Meta-Learning Adaptable Foundation Models—0
Meta-Learning Hyperparameters for Parameter Efficient Fine-Tuning—0
MetaLoRA: Tensor-Enhanced Adaptive Low-Rank Fine-tuning—0
MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning—0
MiLoRA: Efficient Mixture of Low-Rank Adaptation for Large Language Models Fine-tuning—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