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 251–300 of 935 papers

TitleStatusHype
Cross-Modal Adapter for Text-Video RetrievalCode1
AdaMix: Mixture-of-Adaptations for Parameter-efficient Model TuningCode1
Scaling & Shifting Your Features: A New Baseline for Efficient Model TuningCode1
ST-Adapter: Parameter-Efficient Image-to-Video Transfer LearningCode1
AdaMix: Mixture-of-Adaptations for Parameter-efficient Model TuningCode1
Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-TuningCode1
Hyperdecoders: Instance-specific decoders for multi-task NLPCode1
LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot LearnersCode1
Towards a Unified View of Parameter-Efficient Transfer LearningCode1
Efficient Test Time Adapter Ensembling for Low-resource Language VarietiesCode1
BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-modelsCode1
Parameter-efficient Multi-task Fine-tuning for Transformers via Shared HypernetworksCode1
Efficient Adaptation of Pre-trained Vision Transformer underpinned by Approximately Orthogonal Fine-Tuning Strategy—0
LoSiA: Efficient High-Rank Fine-Tuning via Subnet Localization and OptimizationCode0
Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models—0
Optimising Language Models for Downstream Tasks: A Post-Training Perspective—0
Detecting Referring Expressions in Visually Grounded Dialogue with Autoregressive Language ModelsCode0
WordCon: Word-level Typography Control in Scene Text Rendering—0
Exploring Adapter Design Tradeoffs for Low Resource Music Generation—0
ARD-LoRA: Dynamic Rank Allocation for Parameter-Efficient Fine-Tuning of Foundation Models with Heterogeneous Adaptation Needs—0
Memba: Membrane-driven Parameter-Efficient Fine-Tuning for MambaCode0
GuiLoMo: Allocating Expert Number and Rank for LoRA-MoE via Bilevel Optimization with GuidedSelection VectorsCode0
Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters—0
Prefix-Tuning+: Modernizing Prefix-Tuning by Decoupling the Prefix from Attention—0
Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning—0
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
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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