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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
SIBO: A Simple Booster for Parameter-Efficient Fine-Tuning—0
Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources—0
GNNavi: Navigating the Information Flow in Large Language Models by Graph Neural NetworkCode0
Efficiency at Scale: Investigating the Performance of Diminutive Language Models in Clinical Tasks—0
Generalizability of Mixture of Domain-Specific Adapters from the Lens of Signed Weight Directions and its Application to Effective Model Pruning—0
Quantified Task Misalignment to Inform PEFT: An Exploration of Domain Generalization and Catastrophic Forgetting in CLIP—0
LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation—0
L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models—0
Low-rank Attention Side-Tuning for Parameter-Efficient Fine-Tuning—0
Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric LearningCode0
Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning—0
From PEFT to DEFT: Parameter Efficient Finetuning for Reducing Activation Density in TransformersCode0
LoTR: Low Tensor Rank Weight Adaptation—0
Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model—0
X-PEFT: eXtremely Parameter-Efficient Fine-Tuning for Extreme Multi-Profile Scenarios—0
The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness—0
PRILoRA: Pruned and Rank-Increasing Low-Rank Adaptation—0
FinSQL: Model-Agnostic LLMs-based Text-to-SQL Framework for Financial Analysis—0
OrchMoE: Efficient Multi-Adapter Learning with Task-Skill Synergy—0
Adapters Mixup: Mixing Parameter-Efficient Adapters to Enhance the Adversarial Robustness of Fine-tuned Pre-trained Text Classifiers—0
Improving Domain Adaptation through Extended-Text Reading Comprehension—0
Scaling Laws for Forgetting When Fine-Tuning Large Language Models—0
Chain of History: Learning and Forecasting with LLMs for Temporal Knowledge Graph Completion—0
A Survey on Efficient Federated Learning Methods for Foundation Model Training—0
Chain of LoRA: Efficient Fine-tuning of Language Models via Residual 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