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 726–750 of 935 papers

TitleStatusHype
LACoS-BLOOM: Low-rank Adaptation with Contrastive objective on 8 bits Siamese-BLOOM—0
Language and Task Arithmetic with Parameter-Efficient Layers for Zero-Shot Summarization—0
LayerNorm: A key component in parameter-efficient fine-tuning—0
Learning to Route for Dynamic Adapter Composition in Continual Learning with Language Models—0
LeMo: Enabling LEss Token Involvement for MOre Context Fine-tuning—0
Less is More: Extreme Gradient Boost Rank-1 Adaption for Efficient Finetuning of LLMs—0
Let's Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large Language Model—0
Lifelong Learning with Task-Specific Adaptation: Addressing the Stability-Plasticity Dilemma—0
LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning—0
LLMI3D: Empowering LLM with 3D Perception from a Single 2D Image—0
LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient Fine-Tuning—0
LoFT: Low-Rank Adaptation That Behaves Like Full Fine-Tuning—0
LoKi: Low-dimensional KAN for Efficient Fine-tuning Image Models—0
LoKO: Low-Rank Kalman Optimizer for Online Fine-Tuning of Large Models—0
LoRA as a Flexible Framework for Securing Large Vision Systems—0
LoRACode: LoRA Adapters for Code Embeddings—0
LoRA Diffusion: Zero-Shot LoRA Synthesis for Diffusion Model Personalization—0
LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation—0
LoRA Dropout as a Sparsity Regularizer for Overfitting Control—0
LoRA ensembles for large language model fine-tuning—0
LoRA-FAIR: Federated LoRA Fine-Tuning with Aggregation and Initialization Refinement—0
LoRAGuard: An Effective Black-box Watermarking Approach for LoRAs—0
LoRA-Mini : Adaptation Matrices Decomposition and Selective Training—0
LoRA-X: Bridging Foundation Models with Training-Free Cross-Model Adaptation—0
LORD: Low Rank Decomposition Of Monolingual Code LLMs For One-Shot Compression—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