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 221–230 of 935 papers

Title	Date	Tasks	Status	Hype
Fine Tuning without Catastrophic Forgetting via Selective Low Rank Adaptation	Jan 26, 2025	parameter-efficient fine-tuning	—Unverified	0
Speech Translation Refinement using Large Language Models	Jan 25, 2025	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	0
Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification	Jan 25, 2025	Few-Shot Learningparameter-efficient fine-tuning	—Unverified	0
Domain Expansion: Parameter-Efficient Modules as Building Blocks for Composite Domains	Jan 24, 2025	parameter-efficient fine-tuning	CodeCode Available	0
Adaptive Rank Allocation for Federated Parameter-Efficient Fine-Tuning of Language Models	Jan 24, 2025	Federated Learningparameter-efficient fine-tuning	—Unverified	0
Low-Rank Adapters Meet Neural Architecture Search for LLM Compression	Jan 23, 2025	Neural Architecture Searchparameter-efficient fine-tuning	—Unverified	0
Parameter-Efficient Fine-Tuning for Foundation Models	Jan 23, 2025	parameter-efficient fine-tuningSurvey	CodeCode Available	2
EDoRA: Efficient Weight-Decomposed Low-Rank Adaptation via Singular Value Decomposition	Jan 21, 2025	parameter-efficient fine-tuning	CodeCode Available	0
Is your LLM trapped in a Mental Set? Investigative study on how mental sets affect the reasoning capabilities of LLMs	Jan 21, 2025	GSM8KIn-Context Learning	—Unverified	0
OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning	Jan 17, 2025	Computational EfficiencyDiversity	—Unverified	0

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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