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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 601–625 of 935 papers

TitleStatusHype
PEFTT: Parameter-Efficient Fine-Tuning for low-resource Tibetan pre-trained language models—0
PEMT: Multi-Task Correlation Guided Mixture-of-Experts Enables Parameter-Efficient Transfer Learning—0
PERFT: Parameter-Efficient Routed Fine-Tuning for Mixture-of-Expert Model—0
PeriodicLoRA: Breaking the Low-Rank Bottleneck in LoRA Optimization—0
Permissioned LLMs: Enforcing Access Control in Large Language Models—0
Personalized Federated Fine-tuning for Heterogeneous Data: An Automatic Rank Learning Approach via Two-Level LoRA—0
Personalized Text Generation with Contrastive Activation Steering—0
PETALface: Parameter Efficient Transfer Learning for Low-resolution Face Recognition—0
PETapter: Leveraging PET-style classification heads for modular few-shot parameter-efficient fine-tuning—0
pFedMxF: Personalized Federated Class-Incremental Learning with Mixture of Frequency Aggregation—0
Pluto and Charon: A Time and Memory Efficient Collaborative Edge AI Framework for Personal LLMs Fine-Tuning—0
Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks—0
PortLLM: Personalizing Evolving Large Language Models with Training-Free and Portable Model Patches—0
Position-Aware Parameter Efficient Fine-Tuning Approach for Reducing Positional Bias in LLMs—0
Prefix-Tuning+: Modernizing Prefix-Tuning by Decoupling the Prefix from Attention—0
PreQuant: A Task-agnostic Quantization Approach for Pre-trained Language Models—0
Pre-Trained Vision-Language Models as Partial Annotators—0
Pre-training Everywhere: Parameter-Efficient Fine-Tuning for Medical Image Analysis via Target Parameter Pre-training—0
PRILoRA: Pruned and Rank-Increasing Low-Rank Adaptation—0
Understanding and Improving Transfer Learning of Deep Models via Neural Collapse—0
Privacy Preserving Conversion Modeling in Data Clean Room—0
Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for Medical Image Classification—0
Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning—0
Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models—0
Project-Probe-Aggregate: Efficient Fine-Tuning for Group Robustness—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