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 751–800 of 935 papers

TitleStatusHype
Context-PEFT: Efficient Multi-Modal, Multi-Task Fine-Tuning—0
Extending Whisper with prompt tuning to target-speaker ASRCode1
ICL Markup: Structuring In-Context Learning using Soft-Token Tags—0
GIST: Improving Parameter Efficient Fine Tuning via Knowledge InteractionCode1
Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 KilobytesCode1
Aligner: One Global Token is Worth Millions of Parameters When Aligning Large Language Models—0
Fine-tuning vision foundation model for crack segmentation in civil infrastructures—0
Customizable Combination of Parameter-Efficient Modules for Multi-Task Learning—0
MoSA: Mixture of Sparse Adapters for Visual Efficient TuningCode1
mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUsCode2
Evaluating General Purpose Vision Foundation Models for Medical Image Analysis: An Experimental Study of DINOv2 on Radiology BenchmarksCode1
CoLLiE: Collaborative Training of Large Language Models in an Efficient WayCode2
Generative Parameter-Efficient Fine-TuningCode1
PEFTDebias : Capturing debiasing information using PEFTs—0
HiFi Tuner: High-Fidelity Subject-Driven Fine-Tuning for Diffusion Models—0
RaDialog: A Large Vision-Language Model for Radiology Report Generation and Conversational AssistanceCode1
Efficient Stitchable Task AdaptationCode0
A Rank Stabilization Scaling Factor for Fine-Tuning with LoRA—0
I-MedSAM: Implicit Medical Image Segmentation with Segment AnythingCode1
Parameter Efficient Fine-tuning via Cross Block Orchestration for Segment Anything Model—0
Towards Better Parameter-Efficient Fine-Tuning for Large Language Models: A Position Paper—0
ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and QuantizationCode1
PEFT-MedAware: Large Language Model for Medical Awareness—0
To be or not to be? an exploration of continuously controllable prompt engineering—0
Language and Task Arithmetic with Parameter-Efficient Layers for Zero-Shot Summarization—0
Assessing Translation capabilities of Large Language Models involving English and Indian Languages—0
HeLM: Highlighted Evidence augmented Language Model for Enhanced Table-to-Text Generation—0
On the Analysis of Cross-Lingual Prompt Tuning for Decoder-based Multilingual Model—0
Low-Rank Adaptation for Multilingual Summarization: An Empirical Study—0
SAMIHS: Adaptation of Segment Anything Model for Intracranial Hemorrhage SegmentationCode1
PEMA: An Offsite-Tunable Plug-in External Memory Adaptation for Language ModelsCode0
Aggregate, Decompose, and Fine-Tune: A Simple Yet Effective Factor-Tuning Method for Vision TransformerCode1
Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse FinetuningCode0
S-LoRA: Serving Thousands of Concurrent LoRA AdaptersCode3
BioInstruct: Instruction Tuning of Large Language Models for Biomedical Natural Language Processing—0
FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation Models with Mobile Edge Computing—0
Content-based Controls For Music Large Language ModelingCode1
The Expressive Power of Low-Rank AdaptationCode1
Improving Few-shot Generalization of Safety Classifiers via Data Augmented Parameter-Efficient Fine-Tuning—0
Mixture-of-Linguistic-Experts Adapters for Improving and Interpreting Pre-trained Language Models—0
Improving generalization in large language models by learning prefix subspacesCode0
Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editing—0
When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical ApplicationsCode1
Towards a General Framework for Continual Learning with Pre-trainingCode1
Identifying and Adapting Transformer-Components Responsible for Gender Bias in an English Language ModelCode0
Prototype-based HyperAdapter for Sample-Efficient Multi-task TuningCode0
Non-Intrusive Adaptation: Input-Centric Parameter-efficient Fine-Tuning for Versatile Multimodal Modeling—0
Domain Generalization Using Large Pretrained Models with Mixture-of-AdaptersCode1
FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language ModelsCode2
AutoVP: An Automated Visual Prompting Framework and BenchmarkCode1
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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