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 801–825 of 935 papers

TitleStatusHype
NOTE: Notable generation Of patient Text summaries through Efficient approach based on direct preference optimization—0
Offline Policy Learning via Skill-step Abstraction for Long-horizon Goal-Conditioned Tasks—0
OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning—0
On Fairness of Task Arithmetic: The Role of Task Vectors—0
On the Analysis of Cross-Lingual Prompt Tuning for Decoder-based Multilingual Model—0
Towards Inducing Document-Level Abilities in Standard Multilingual Neural Machine Translation Models—0
Optimising Language Models for Downstream Tasks: A Post-Training Perspective—0
Optimization-Inspired Few-Shot Adaptation for Large Language Models—0
Optimizing Language Models for Grammatical Acceptability: A Comparative Study of Fine-Tuning Techniques—0
OrchMoE: Efficient Multi-Adapter Learning with Task-Skill Synergy—0
OSoRA: Output-Dimension and Singular-Value Initialized Low-Rank Adaptation—0
Parameter-Efficient Active Learning for Foundational models—0
Parameter-efficient Adaptation of Multilingual Multimodal Models for Low-resource ASR—0
Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation—0
Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging—0
Parameter-Efficient Continual Fine-Tuning: A Survey—0
Parameter Efficient Continual Learning with Dynamic Low-Rank Adaptation—0
Parameter Efficient Fine Tuning: A Comprehensive Analysis Across Applications—0
Parameter-Efficient Fine-Tuning Design Spaces—0
Parameter-Efficient Fine-Tuning for Continual Learning: A Neural Tangent Kernel Perspective—0
Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey—0
Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity—0
Parameter Efficient Fine Tuning for Multi-scanner PET to PET Reconstruction—0
Parameter-efficient Fine-tuning in Hyperspherical Space for Open-vocabulary Semantic Segmentation—0
Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies—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