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 301–325 of 935 papers

TitleStatusHype
Improving LoRA in Privacy-preserving Federated Learning—0
Efficient In-Domain Question Answering for Resource-Constrained Environments—0
Block-wise LoRA: Revisiting Fine-grained LoRA for Effective Personalization and Stylization in Text-to-Image Generation—0
Efficient Federated Split Learning for Large Language Models over Communication Networks—0
Block Expanded DINORET: Adapting Natural Domain Foundation Models for Retinal Imaging Without Catastrophic Forgetting—0
AMR Parsing with Instruction Fine-tuned Pre-trained Language Models—0
Is your LLM trapped in a Mental Set? Investigative study on how mental sets affect the reasoning capabilities of LLMs—0
Efficient Federated Fine-Tuning of Large Language Models with Layer Dropout—0
Efficient Federated Class-Incremental Learning of Pre-Trained Models via Task-agnostic Low-rank Residual Adaptation—0
Black Sheep in the Herd: Playing with Spuriously Correlated Attributes for Vision-Language Recognition—0
Efficient Differentially Private Fine-Tuning of Diffusion Models—0
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models—0
Adapter-X: A Novel General Parameter-Efficient Fine-Tuning Framework for Vision—0
Efficient Deployment of Large Language Models on Resource-constrained Devices—0
Efficient Continual Adaptation of Pretrained Robotic Policy with Online Meta-Learned Adapters—0
Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models—0
Efficient and Effective Adaptation of Multimodal Foundation Models in Sequential Recommendation—0
BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models—0
Memory-Efficient Orthogonal Fine-Tuning with Principal Subspace Adaptation—0
Efficient Adaptation of Pre-trained Vision Transformer underpinned by Approximately Orthogonal Fine-Tuning Strategy—0
Efficient Adaptation of Pre-trained Vision Transformer via Householder Transformation—0
BiSup: Bidirectional Quantization Error Suppression for Large Language Models—0
Efficient Adaptation For Remote Sensing Visual Grounding—0
Efficiency in Focus: LayerNorm as a Catalyst for Fine-tuning Medical Visual Language Pre-trained Models—0
BIPEFT: Budget-Guided Iterative Search for Parameter Efficient Fine-Tuning of Large Pretrained Language Models—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