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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 276–300 of 935 papers

TitleStatusHype
FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models—0
FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models—0
MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning—0
AR-RAG: Autoregressive Retrieval Augmentation for Image GenerationCode0
Mitigating Catastrophic Forgetting with Adaptive Transformer Block Expansion in Federated Fine-Tuning—0
InstantFT: An FPGA-Based Runtime Subsecond Fine-tuning of CNN Models—0
Gradient Inversion Attacks on Parameter-Efficient Fine-TuningCode0
Leveraging Coordinate Momentum in SignSGD and Muon: Memory-Optimized Zero-OrderCode0
Matching Markets Meet LLMs: Algorithmic Reasoning with Ranked Preferences—0
WeightLoRA: Keep Only Necessary Adapters—0
Parameter Efficient Fine Tuning Llama 3.1 for Answering Arabic Legal Questions: A Case Study on Jordanian LawsCode0
Uni-LoRA: One Vector is All You Need—0
Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection—0
LoRA as a Flexible Framework for Securing Large Vision Systems—0
On Fairness of Task Arithmetic: The Role of Task Vectors—0
Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You NeedCode0
Beyond Zero Initialization: Investigating the Impact of Non-Zero Initialization on LoRA Fine-Tuning DynamicsCode0
SC-LoRA: Balancing Efficient Fine-tuning and Knowledge Preservation via Subspace-Constrained LoRA—0
Noise-Robustness Through Noise: Asymmetric LoRA Adaption with Poisoning Expert—0
Zero-Shot Adaptation of Parameter-Efficient Fine-Tuning in Diffusion Models—0
MAP: Revisiting Weight Decomposition for Low-Rank Adaptation—0
Weight Spectra Induced Efficient Model Adaptation—0
Train with Perturbation, Infer after Merging: A Two-Stage Framework for Continual Learning—0
InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective—0
Permissioned LLMs: Enforcing Access Control in Large Language Models—0
Show:102550
← PrevPage 12 of 38Next →
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