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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 751–800 of 935 papers

TitleStatusHype
Optimization-Inspired Few-Shot Adaptation for Large Language Models—0
Optimizing Language Models for Grammatical Acceptability: A Comparative Study of Fine-Tuning Techniques—0
OrchMoE: Efficient Multi-Adapter Learning with Task-Skill Synergy—0
OSoRA: Output-Dimension and Singular-Value Initialized Low-Rank Adaptation—0
Parameter-Efficient Active Learning for Foundational models—0
Parameter-efficient Adaptation of Multilingual Multimodal Models for Low-resource ASR—0
Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation—0
Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging—0
On-Device LLM for Context-Aware Wi-Fi RoamingCode0
NLoRA: Nyström-Initiated Low-Rank Adaptation for Large Language ModelsCode0
Navigating the Landscape of Large Language Models: A Comprehensive Review and Analysis of Paradigms and Fine-Tuning StrategiesCode0
GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMsCode0
ROSA: Random Subspace Adaptation for Efficient Fine-TuningCode0
Music for All: Representational Bias and Cross-Cultural Adaptability of Music Generation ModelsCode0
RoseLoRA: Row and Column-wise Sparse Low-rank Adaptation of Pre-trained Language Model for Knowledge Editing and Fine-tuningCode0
Multi-View and Multi-Scale Alignment for Contrastive Language-Image Pre-training in MammographyCode0
MU-Bench: A Multitask Multimodal Benchmark for Machine UnlearningCode0
From PEFT to DEFT: Parameter Efficient Finetuning for Reducing Activation Density in TransformersCode0
CLIP-SLA: Parameter-Efficient CLIP Adaptation for Continuous Sign Language RecognitionCode0
FLoRA: Enhancing Vision-Language Models with Parameter-Efficient Federated LearningCode0
Orchid2024: A cultivar-level dataset and methodology for fine-grained classification of Chinese Cymbidium OrchidsCode0
CLIP-IT: CLIP-based Pairing for Histology Images ClassificationCode0
RCA: Region Conditioned Adaptation for Visual Abductive ReasoningCode0
MSPLoRA: A Multi-Scale Pyramid Low-Rank Adaptation for Efficient Model Fine-TuningCode0
MotionAura: Generating High-Quality and Motion Consistent Videos using Discrete DiffusionCode0
15,500 Seconds: Lean UAV Classification Leveraging PEFT and Pre-Trained NetworksCode0
Fighting Randomness with Randomness: Mitigating Optimisation Instability of Fine-Tuning using Delayed Ensemble and Noisy InterpolationCode0
FarExStance: Explainable Stance Detection for FarsiCode0
Extending LLMs to New Languages: A Case Study of Llama and Persian AdaptationCode0
CLaDMoP: Learning Transferrable Models from Successful Clinical Trials via LLMsCode0
Exploring the Benefits of Differentially Private Pre-training and Parameter-Efficient Fine-tuning for Table TransformersCode0
Parameter-Efficient Cross-lingual Transfer of Vision and Language Models via Translation-based AlignmentCode0
Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse FinetuningCode0
SBoRA: Low-Rank Adaptation with Regional Weight UpdatesCode0
MoRE: A Mixture of Low-Rank Experts for Adaptive Multi-Task LearningCode0
TeamLoRA: Boosting Low-Rank Adaptation with Expert Collaboration and CompetitionCode0
Parameter-efficient Fine-tuning for improved Convolutional Baseline for Brain Tumor Segmentation in Sub-Saharan Africa Adult Glioma DatasetCode0
Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-TuningCode0
Tell Me Why: Explainable Public Health Fact-Checking with Large Language ModelsCode0
Exploring Sparsity for Parameter Efficient Fine Tuning Using WaveletsCode0
MoLEx: Mixture of Layer Experts for Finetuning with Sparse UpcyclingCode0
Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based AdaptationCode0
An efficient framework based on large foundation model for cervical cytopathology whole slide image screeningCode0
Minimal Ranks, Maximum Confidence: Parameter-efficient Uncertainty Quantification for LoRACode0
Parameter Efficient Fine Tuning Llama 3.1 for Answering Arabic Legal Questions: A Case Study on Jordanian LawsCode0
A Comprehensive Evaluation of Parameter-Efficient Fine-Tuning on Software Engineering TasksCode0
Empirical Studies of Parameter Efficient Methods for Large Language Models of Code and Knowledge Transfer to RCode0
PoliTune: Analyzing the Impact of Data Selection and Fine-Tuning on Economic and Political Biases in Large Language ModelsCode0
Parameter-Efficient Finetuning of Transformers for Source CodeCode0
SDPT: Synchronous Dual Prompt Tuning for Fusion-based Visual-Language Pre-trained ModelsCode0
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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