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 576–600 of 935 papers

TitleStatusHype
Are Large Language Models State-of-the-art Quality Estimators for Machine Translation of User-generated Content?Code0
QERA: an Analytical Framework for Quantization Error Reconstruction—0
DiDOTS: Knowledge Distillation from Large-Language-Models for Dementia Obfuscation in Transcribed Speech—0
LoRTA: Low Rank Tensor Adaptation of Large Language Models—0
BIPEFT: Budget-Guided Iterative Search for Parameter Efficient Fine-Tuning of Large Pretrained Language Models—0
Adapting Segment Anything Model to Melanoma Segmentation in Microscopy Slide Images—0
Llama SLayer 8B: Shallow Layers Hold the Key to Knowledge InjectionCode0
NEAT: Nonlinear Parameter-efficient Adaptation of Pre-trained Models—0
House of Cards: Massive Weights in LLMs—0
DLP-LoRA: Efficient Task-Specific LoRA Fusion with a Dynamic, Lightweight Plugin for Large Language ModelsCode0
Embedding-based statistical inference on generative models—0
SQFT: Low-cost Model Adaptation in Low-precision Sparse Foundation Models—0
Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language ModelsCode0
Unsupervised Human Preference Learning—0
Resource Allocation for Stable LLM Training in Mobile Edge Computing—0
Pear: Pruning and Sharing Adapters in Visual Parameter-Efficient Fine-TuningCode0
FINE: Factorizing Knowledge for Initialization of Variable-sized Diffusion Models—0
A GEN AI Framework for Medical Note Generation—0
HM3: Heterogeneous Multi-Class Model Merging—0
PEDRO: Parameter-Efficient Fine-tuning with Prompt DEpenDent Representation MOdification—0
Multi-View and Multi-Scale Alignment for Contrastive Language-Image Pre-training in MammographyCode0
Efficient In-Domain Question Answering for Resource-Constrained Environments—0
Exploring Acoustic Similarity in Emotional Speech and Music via Self-Supervised Representations—0
Weak-to-Strong Backdoor Attack for Large Language Models—0
Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation—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