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 201–225 of 935 papers

TitleStatusHype
MoLoRec: A Generalizable and Efficient Framework for LLM-Based Recommendation—0
LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits—0
Music for All: Representational Bias and Cross-Cultural Adaptability of Music Generation ModelsCode0
Hyper Compressed Fine-Tuning of Large Foundation Models with Quantum Inspired Adapters—0
Model Diffusion for Certifiable Few-shot Transfer Learning—0
SSMLoRA: Enhancing Low-Rank Adaptation with State Space ModelCode1
ULPT: Prompt Tuning with Ultra-Low-Dimensional Optimization—0
LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient Fine-Tuning—0
Bilevel ZOFO: Bridging Parameter-Efficient and Zeroth-Order Techniques for Efficient LLM Fine-Tuning and Meta-Training—0
FedP^2EFT: Federated Learning to Personalize Parameter Efficient Fine-Tuning for Multilingual LLMs—0
RandLoRA: Full-rank parameter-efficient fine-tuning of large models—0
Joint Localization and Activation Editing for Low-Resource Fine-TuningCode1
Robust Federated Finetuning of LLMs via Alternating Optimization of LoRA—0
Parameter Efficient Fine-Tuning of Segment Anything ModelCode1
Norm-Bounded Low-Rank Adaptation—0
Enhancing Large Language Model Efficiencyvia Symbolic Compression: A Formal Approach Towards Interpretability—0
High-Accuracy ECG Image Interpretation using Parameter-Efficient LoRA Fine-Tuning with Multimodal LLaMA 3.2—0
LoRA-X: Bridging Foundation Models with Training-Free Cross-Model Adaptation—0
LoRAGuard: An Effective Black-box Watermarking Approach for LoRAs—0
Fine Tuning without Catastrophic Forgetting via Selective Low Rank Adaptation—0
Decentralized Low-Rank Fine-Tuning of Large Language Models—0
Speech Translation Refinement using Large Language ModelsCode0
Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification—0
Domain Expansion: Parameter-Efficient Modules as Building Blocks for Composite DomainsCode0
Adaptive Rank Allocation for Federated Parameter-Efficient Fine-Tuning of Language Models—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