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 876–900 of 935 papers

TitleStatusHype
Enhancing Multilingual Speech Recognition through Language Prompt Tuning and Frame-Level Language Adapter—0
Scaled Prompt-Tuning for Few-Shot Natural Language Generation—0
Exploring the Benefits of Differentially Private Pre-training and Parameter-Efficient Fine-tuning for Table TransformersCode0
FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning—0
SAM-PARSER: Fine-tuning SAM Efficiently by Parameter Space Reconstruction—0
LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning—0
Comparison between parameter-efficient techniques and full fine-tuning: A case study on multilingual news article classificationCode0
SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models—0
WIKITIDE: A Wikipedia-Based Timestamped Definition Pairs Dataset—0
MA-FSAR: Multimodal Adaptation of CLIP for Few-Shot Action Recognition—0
Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectives—0
DVPT: Dynamic Visual Prompt Tuning of Large Pre-trained Models for Medical Image AnalysisCode0
SuryaKiran at MEDIQA-Sum 2023: Leveraging LoRA for Clinical Dialogue Summarization—0
Prompt to be Consistent is Better than Self-Consistent? Few-Shot and Zero-Shot Fact Verification with Pre-trained Language ModelsCode0
PreQuant: A Task-agnostic Quantization Approach for Pre-trained Language Models—0
Parameter-Efficient Fine-Tuning without Introducing New LatencyCode0
Neural Architecture Search for Parameter-Efficient Fine-tuning of Large Pre-trained Language Models—0
Towards Adaptive Prefix Tuning for Parameter-Efficient Language Model Fine-tuning—0
Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization—0
Parameter-Efficient Language Model Tuning with Active Learning in Low-Resource SettingsCode0
SPARSEFIT: Few-shot Prompting with Sparse Fine-tuning for Jointly Generating Predictions and Natural Language ExplanationsCode0
Ahead-of-Time P-Tuning—0
G-Adapter: Towards Structure-Aware Parameter-Efficient Transfer Learning for Graph Transformer Networks—0
Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity—0
Exploring Zero and Few-shot Techniques for Intent Classification—0
Show:102550
← PrevPage 36 of 38Next →
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