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 651–700 of 935 papers

TitleStatusHype
PEFT-U: Parameter-Efficient Fine-Tuning for User PersonalizationCode0
Parameter-Efficient Fine-Tuning for Continual Learning: A Neural Tangent Kernel Perspective—0
Zero-Shot Embeddings Inform Learning and Forgetting with Vision-Language Encoders—0
Learn to Preserve and Diversify: Parameter-Efficient Group with Orthogonal Regularization for Domain GeneralizationCode0
Turning Generative Models Degenerate: The Power of Data Poisoning Attacks—0
Missing Modality Prediction for Unpaired Multimodal Learning via Joint Embedding of Unimodal Models—0
LoRA-PT: Low-Rank Adapting UNETR for Hippocampus Segmentation Using Principal Tensor Singular Values and VectorsCode0
InstructAV: Instruction Fine-tuning Large Language Models for Authorship VerificationCode0
SDPT: Synchronous Dual Prompt Tuning for Fusion-based Visual-Language Pre-trained ModelsCode0
An efficient framework based on large foundation model for cervical cytopathology whole slide image screeningCode0
Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for Medical Image Classification—0
Low-Rank Interconnected Adaptation across LayersCode0
Parameter Efficient Fine Tuning for Multi-scanner PET to PET Reconstruction—0
ROSA: Random Subspace Adaptation for Efficient Fine-TuningCode0
Reprogramming Distillation for Medical Foundation ModelsCode0
SBoRA: Low-Rank Adaptation with Regional Weight UpdatesCode0
GPT vs RETRO: Exploring the Intersection of Retrieval and Parameter-Efficient Fine-Tuning—0
Deep Content Understanding Toward Entity and Aspect Target Sentiment Analysis on Foundation ModelsCode0
ASteISR: Adapting Single Image Super-resolution Pre-trained Model for Efficient Stereo Image Super-resolutionCode0
Investigating Decoder-only Large Language Models for Speech-to-text Translation—0
CatMemo at the FinLLM Challenge Task: Fine-Tuning Large Language Models using Data Fusion in Financial Applications—0
FineCLIPER: Multi-modal Fine-grained CLIP for Dynamic Facial Expression Recognition with AdaptERs—0
Soft Language Prompts for Language TransferCode0
HyperLoader: Integrating Hypernetwork-Based LoRA and Adapter Layers into Multi-Task Transformers for Sequence Labelling—0
SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models—0
Adapting Multilingual LLMs to Low-Resource Languages with Knowledge Graphs via AdaptersCode0
Structured Unrestricted-Rank Matrices for Parameter Efficient Fine-tuningCode0
Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning—0
Federated Adversarial Learning for Robust Autonomous Landing Runway Detection—0
MU-Bench: A Multitask Multimodal Benchmark for Machine UnlearningCode0
Unlocking the Global Synergies in Low-Rank Adapters—0
Towards Infinite-Long Prefix in TransformerCode0
Fighting Randomness with Randomness: Mitigating Optimisation Instability of Fine-Tuning using Delayed Ensemble and Noisy InterpolationCode0
Bayesian-LoRA: LoRA based Parameter Efficient Fine-Tuning using Optimal Quantization levels and Rank Values trough Differentiable Bayesian Gates—0
ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts—0
RoseLoRA: Row and Column-wise Sparse Low-rank Adaptation of Pre-trained Language Model for Knowledge Editing and Fine-tuningCode0
ShareLoRA: Parameter Efficient and Robust Large Language Model Fine-tuning via Shared Low-Rank AdaptationCode0
Promoting Data and Model Privacy in Federated Learning through Quantized LoRA—0
PC-LoRA: Low-Rank Adaptation for Progressive Model Compression with Knowledge Distillation—0
Parameter-Efficient Active Learning for Foundational models—0
Updating CLIP to Prefer Descriptions Over CaptionsCode0
A Parameter-efficient Language Extension Framework for Multilingual ASR—0
An Improved Empirical Fisher Approximation for Natural Gradient Descent—0
A Survey of Recent Backdoor Attacks and Defenses in Large Language Models—0
Efficient Differentially Private Fine-Tuning of Diffusion Models—0
Time Sensitive Knowledge Editing through Efficient Finetuning—0
VHDL-Eval: A Framework for Evaluating Large Language Models in VHDL Code Generation—0
Hypernetworks for Personalizing ASR to Atypical Speech—0
Choice of PEFT Technique in Continual Learning: Prompt Tuning is Not All You Need—0
Adapter-X: A Novel General Parameter-Efficient Fine-Tuning Framework for Vision—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