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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 551–600 of 935 papers

TitleStatusHype
SVFT: Parameter-Efficient Fine-Tuning with Singular VectorsCode1
RAP: Efficient Text-Video Retrieval with Sparse-and-Correlated Adapter—0
MLAE: Masked LoRA Experts for Visual Parameter-Efficient Fine-TuningCode1
Domain-Inspired Sharpness-Aware Minimization Under Domain ShiftsCode0
MemControl: Mitigating Memorization in Diffusion Models via Automated Parameter SelectionCode0
Parameter-efficient Fine-tuning in Hyperspherical Space for Open-vocabulary Semantic Segmentation—0
Low-Rank Few-Shot Adaptation of Vision-Language ModelsCode3
IAPT: Instruction-Aware Prompt Tuning for Large Language Models—0
Sparsity- and Hybridity-Inspired Visual Parameter-Efficient Fine-Tuning for Medical Diagnosis—0
Semantic are Beacons: A Semantic Perspective for Unveiling Parameter-Efficient Fine-Tuning in Knowledge Learning—0
LoRA-XS: Low-Rank Adaptation with Extremely Small Number of ParametersCode2
DoRA: Enhancing Parameter-Efficient Fine-Tuning with Dynamic Rank DistributionCode0
Safe LoRA: the Silver Lining of Reducing Safety Risks when Fine-tuning Large Language ModelsCode1
Self-Corrected Multimodal Large Language Model for End-to-End Robot Manipulation—0
Trans-LoRA: towards data-free Transferable Parameter Efficient Finetuning—0
SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language ModelsCode1
PatchProt: Hydrophobic patch prediction using protein foundation modelsCode0
Prompt Tuning Strikes Back: Customizing Foundation Models with Low-Rank Prompt AdaptationCode0
Sparse Matrix in Large Language Model Fine-tuningCode1
VB-LoRA: Extreme Parameter Efficient Fine-Tuning with Vector BanksCode1
BiSup: Bidirectional Quantization Error Suppression for Large Language Models—0
Pre-Trained Vision-Language Models as Partial Annotators—0
FLoRA: Low-Rank Core Space for N-dimensionCode1
Sparse-Tuning: Adapting Vision Transformers with Efficient Fine-tuning and InferenceCode1
Spectral Adapter: Fine-Tuning in Spectral SpaceCode1
MoRA: High-Rank Updating for Parameter-Efficient Fine-TuningCode3
FeTT: Continual Class Incremental Learning via Feature Transformation Tuning—0
MeteoRA: Multiple-tasks Embedded LoRA for Large Language ModelsCode1
HARIS: Human-Like Attention for Reference Image Segmentation—0
Tell Me Why: Explainable Public Health Fact-Checking with Large Language ModelsCode0
SPD-CFL: Stepwise Parameter Dropout for Efficient Continual Federated Learning—0
Parameter-Efficient Instance-Adaptive Neural Video CompressionCode1
DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation—0
Selective Fine-tuning on LLM-labeled Data May Reduce Reliance on Human Annotation: A Case Study Using Schedule-of-Event Table Detection—0
Memory-Space Visual Prompting for Efficient Vision-Language Fine-TuningCode2
Parameter-Efficient Fine-Tuning With Adapters—0
CourseGPT-zh: an Educational Large Language Model Based on Knowledge Distillation Incorporating Prompt Optimization—0
ELiTe: Efficient Image-to-LiDAR Knowledge Transfer for Semantic Segmentation—0
Refining Joint Text and Source Code Embeddings for Retrieval Task with Parameter-Efficient Fine-TuningCode0
Parameter-Efficient Fine-Tuning with Discrete Fourier TransformCode2
Enhancing News Summarization with ELearnFit through Efficient In-Context Learning and Efficient Fine-Tuning—0
Random Masking Finds Winning Tickets for Parameter Efficient Fine-tuningCode1
TartuNLP at EvaLatin 2024: Emotion Polarity Detection—0
MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language Models using 2D PriorsCode2
NeMo-Aligner: Scalable Toolkit for Efficient Model AlignmentCode4
Investigating Automatic Scoring and Feedback using Large Language Models—0
RST-LoRA: A Discourse-Aware Low-Rank Adaptation for Long Document Abstractive Summarization—0
MoPEFT: A Mixture-of-PEFTs for the Segment Anything Model—0
SPAFIT: Stratified Progressive Adaptation Fine-tuning for Pre-trained Large Language Models—0
HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-TuningCode3
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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