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 751–775 of 935 papers

TitleStatusHype
Navigating the Landscape of Large Language Models: A Comprehensive Review and Analysis of Paradigms and Fine-Tuning StrategiesCode0
FLoRA: Enhancing Vision-Language Models with Parameter-Efficient Federated LearningCode0
PoliTune: Analyzing the Impact of Data Selection and Fine-Tuning on Economic and Political Biases in Large Language ModelsCode0
Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models—0
Using Few-Shot Learning to Classify Primary Lung Cancer and Other Malignancy with Lung Metastasis in Cytological Imaging via Endobronchial Ultrasound Procedures—0
DLoRA: Distributed Parameter-Efficient Fine-Tuning Solution for Large Language Model—0
Certified PEFTSmoothing: Parameter-Efficient Fine-Tuning with Randomized Smoothing—0
Q-PEFT: Query-dependent Parameter Efficient Fine-tuning for Text Reranking with Large Language Models—0
Unlocking Parameter-Efficient Fine-Tuning for Low-Resource Language Translation—0
Personalized LLM Response Generation with Parameterized Memory InjectionCode0
GP-MoLFormer: A Foundation Model For Molecular Generation—0
Enhancing Low-Resource LLMs Classification with PEFT and Synthetic Data—0
Position-Aware Parameter Efficient Fine-Tuning Approach for Reducing Positional Bias in LLMs—0
Harnessing the Power of Large Language Model for Uncertainty Aware Graph ProcessingCode0
Query-driven Relevant Paragraph Extraction from Legal Judgments—0
Edinburgh Clinical NLP at SemEval-2024 Task 2: Fine-tune your model unless you have access to GPT-4Code0
LayerNorm: A key component in parameter-efficient fine-tuning—0
Is Modularity Transferable? A Case Study through the Lens of Knowledge DistillationCode0
ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models—0
A Single Linear Layer Yields Task-Adapted Low-Rank Matrices—0
Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey—0
AFLoRA: Adaptive Freezing of Low Rank Adaptation in Parameter Efficient Fine-Tuning of Large Models—0
AdaViPro: Region-based Adaptive Visual Prompt for Large-Scale Models Adapting—0
AdaFish: Fast low-rank parameter-efficient fine-tuning by using second-order information—0
Improving LoRA in Privacy-preserving Federated 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