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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 301–350 of 935 papers

TitleStatusHype
Promptable Anomaly Segmentation with SAM Through Self-Perception Tuning—0
Towards Efficient Model-Heterogeneity Federated Learning for Large Models—0
Graph Adapter of EEG Foundation Models for Parameter Efficient Fine Tuning—0
Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models—0
LoRA-Mini : Adaptation Matrices Decomposition and Selective Training—0
LoRA-FAIR: Federated LoRA Fine-Tuning with Aggregation and Initialization Refinement—0
Exploring Foundation Models Fine-Tuning for Cytology ClassificationCode1
Parameter Efficient Mamba Tuning via Projector-targeted Diagonal-centric Linear Transformation—0
Visual Cue Enhancement and Dual Low-Rank Adaptation for Efficient Visual Instruction Fine-Tuning—0
F^3OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics—0
HELENE: Hessian Layer-wise Clipping and Gradient Annealing for Accelerating Fine-tuning LLM with Zeroth-order Optimization—0
PERFT: Parameter-Efficient Routed Fine-Tuning for Mixture-of-Expert Model—0
Federated Low-Rank Adaptation with Differential Privacy over Wireless Networks—0
MapSAM: Adapting Segment Anything Model for Automated Feature Detection in Historical MapsCode1
Prompt-Efficient Fine-Tuning for GPT-like Deep Models to Reduce Hallucination and to Improve Reproducibility in Scientific Text Generation Using Stochastic Optimisation Techniques—0
CULL-MT: Compression Using Language and Layer pruning for Machine Translation—0
Robust and Efficient Fine-tuning of LLMs with Bayesian Reparameterization of Low-Rank AdaptationCode0
MambaPEFT: Exploring Parameter-Efficient Fine-Tuning for MambaCode1
Efficient and Effective Adaptation of Multimodal Foundation Models in Sequential Recommendation—0
Parameter-Efficient Fine-Tuning of Large Language Models for Unit Test Generation: An Empirical Study—0
Expanding Sparse Tuning for Low Memory UsageCode1
C2A: Client-Customized Adaptation for Parameter-Efficient Federated LearningCode1
Dual Low-Rank Adaptation for Continual Learning with Pre-Trained Models—0
Is Multiple Object Tracking a Matter of Specialization?—0
CleaR: Towards Robust and Generalized Parameter-Efficient Fine-Tuning for Noisy Label LearningCode0
Parameter-Efficient Fine-Tuning Medical Multimodal Large Language Models for Medical Visual Grounding—0
EF-LLM: Energy Forecasting LLM with AI-assisted Automation, Enhanced Sparse Prediction, Hallucination Detection—0
Efficient Adaptation of Pre-trained Vision Transformer via Householder Transformation—0
MALoRA: Mixture of Asymmetric Low-Rank Adaptation for Enhanced Multi-Task Learning—0
Exploring Gradient Subspaces: Addressing and Overcoming LoRA's Limitations in Federated Fine-Tuning of Large Language Models—0
Capacity Control is an Effective Memorization Mitigation Mechanism in Text-Conditional Diffusion ModelsCode0
Meta-Learning Adaptable Foundation Models—0
IntLoRA: Integral Low-rank Adaptation of Quantized Diffusion ModelsCode1
KD-LoRA: A Hybrid Approach to Efficient Fine-Tuning with LoRA and Knowledge DistillationCode1
Get Large Language Models Ready to Speak: A Late-fusion Approach for Speech Generation—0
Less is More: Extreme Gradient Boost Rank-1 Adaption for Efficient Finetuning of LLMs—0
Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies—0
GeoLoRA: Geometric integration for parameter efficient fine-tuning—0
MiLoRA: Efficient Mixture of Low-Rank Adaptation for Large Language Models Fine-tuning—0
Towards Real Zero-Shot Camouflaged Object Segmentation without Camouflaged AnnotationsCode0
FairLoRA: Unpacking Bias Mitigation in Vision Models with Fairness-Driven Low-Rank Adaptation—0
Benchmarking Pathology Foundation Models: Adaptation Strategies and ScenariosCode0
Natural GaLore: Accelerating GaLore for memory-efficient LLM Training and Fine-tuningCode1
The effect of fine-tuning on language model toxicityCode0
MIRA: A Method of Federated MultI-Task Learning for LaRge LAnguage Models—0
LoRA-IR: Taming Low-Rank Experts for Efficient All-in-One Image RestorationCode2
Unlearning Backdoor Attacks for LLMs with Weak-to-Strong Knowledge DistillationCode0
LoLDU: Low-Rank Adaptation via Lower-Diag-Upper Decomposition for Parameter-Efficient Fine-TuningCode0
Parameter-efficient Adaptation of Multilingual Multimodal Models for Low-resource ASR—0
Unlocking the Capabilities of Masked Generative Models for Image Synthesis via Self-GuidanceCode1
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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