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 611–620 of 935 papers

Title	Date	Tasks	Status
Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models	Nov 24, 2024	Data Poisoningparameter-efficient fine-tuning	—Unverified
Efficient Continual Adaptation of Pretrained Robotic Policy with Online Meta-Learned Adapters	Mar 24, 2025	Meta-Learningparameter-efficient fine-tuning	—Unverified
Efficient Deployment of Large Language Models on Resource-constrained Devices	Jan 5, 2025	Federated Learningparameter-efficient fine-tuning	—Unverified
Efficient Differentially Private Fine-Tuning of Diffusion Models	Jun 7, 2024	parameter-efficient fine-tuning	—Unverified
Efficient Federated Class-Incremental Learning of Pre-Trained Models via Task-agnostic Low-rank Residual Adaptation	May 18, 2025	class-incremental learningClass Incremental Learning	—Unverified
Efficient Federated Fine-Tuning of Large Language Models with Layer Dropout	Mar 13, 2025	parameter-efficient fine-tuning	—Unverified
Efficient Federated Split Learning for Large Language Models over Communication Networks	Apr 20, 2025	Federated Learningparameter-efficient fine-tuning	—Unverified
Efficient In-Domain Question Answering for Resource-Constrained Environments	Sep 26, 2024	parameter-efficient fine-tuningPrompt Engineering	—Unverified
Efficient Telecom Specific LLM: TSLAM-Mini with QLoRA and Digital Twin Data	May 10, 2025	Instruction Followingparameter-efficient fine-tuning	—Unverified
EF-LLM: Energy Forecasting LLM with AI-assisted Automation, Enhanced Sparse Prediction, Hallucination Detection	Oct 30, 2024	Continual LearningHallucination	—Unverified

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All datasets BoolQ HellaSwag WinoGrande

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	LLaMA2-7b	Accuracy (% )	82.63	—	Unverified
2	LLaMA2-7b	Accuracy (% )	82.63	—	Unverified
3	LLaMA2-7b	Accuracy (% )	81.93	—	Unverified
4	LLaMA2-7b	Accuracy (% )	80.28	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LLaMA2-7b	Accuracy (% )	76.68	—	Unverified
2	LLaMA2-7b	Accuracy (% )	76.67	—	Unverified
3	LLaMA2-7b	Accuracy (% )	76.27	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LLaMA2-7b	Accuracy (% )	70.8	—	Unverified
2	LLaMA2-7b	Accuracy (% )	70.09	—	Unverified
3	LLaMA2-7b	Accuracy (% )	69.85	—	Unverified