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 351–400 of 935 papers

TitleStatusHype
Prompt Compression for Large Language Models: A SurveyCode1
LoRA Soups: Merging LoRAs for Practical Skill Composition TasksCode1
Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models—0
LoKO: Low-Rank Kalman Optimizer for Online Fine-Tuning of Large Models—0
Sequential LLM Framework for Fashion Recommendation—0
Layer-wise Importance Matters: Less Memory for Better Performance in Parameter-efficient Fine-tuning of Large Language ModelsCode0
AlphaLoRA: Assigning LoRA Experts Based on Layer Training QualityCode1
RoCoFT: Efficient Finetuning of Large Language Models with Row-Column UpdatesCode0
BiDoRA: Bi-level Optimization-Based Weight-Decomposed Low-Rank Adaptation—0
Towards Efficient Visual-Language Alignment of the Q-Former for Visual Reasoning TasksCode1
MTL-LoRA: Low-Rank Adaptation for Multi-Task LearningCode1
DARE the Extreme: Revisiting Delta-Parameter Pruning For Fine-Tuned ModelsCode0
SLiM: One-shot Quantization and Sparsity with Low-rank Approximation for LLM Weight CompressionCode1
Parameter-Efficient Fine-Tuning of State Space ModelsCode1
QEFT: Quantization for Efficient Fine-Tuning of LLMsCode0
Parameter-Efficient Fine-Tuning of Large Language Models using Semantic Knowledge Tuning—0
ACCEPT: Adaptive Codebook for Composite and Efficient Prompt TuningCode0
Enhancing Zeroth-order Fine-tuning for Language Models with Low-rank StructuresCode1
Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptation—0
MotionAura: Generating High-Quality and Motion Consistent Videos using Discrete DiffusionCode0
Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud LearningCode3
SLIM: Let LLM Learn More and Forget Less with Soft LoRA and Identity Mixture—0
Parameter-Efficient Fine-Tuning via Selective Discrete Cosine Transform—0
SparseGrad: A Selective Method for Efficient Fine-tuning of MLP LayersCode0
Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs—0
Parameter Efficient Fine-tuning via Explained Variance AdaptationCode1
PortLLM: Personalizing Evolving Large Language Models with Training-Free and Portable Model Patches—0
CASA: Class-Agnostic Shared Attributes in Vision-Language Models for Efficient Incremental Object Detection—0
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
TPP-LLM: Modeling Temporal Point Processes by Efficiently Fine-Tuning Large Language ModelsCode1
Imaging foundation model for universal enhancement of non-ideal measurement CTCode1
DLP-LoRA: Efficient Task-Specific LoRA Fusion with a Dynamic, Lightweight Plugin for Large Language ModelsCode0
SQFT: Low-cost Model Adaptation in Low-precision Sparse Foundation ModelsCode0
Embedding-based statistical inference on generative models—0
Unsupervised Human Preference Learning—0
Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language ModelsCode0
Resource Allocation for Stable LLM Training in Mobile Edge Computing—0
Vision-Language Models are Strong Noisy Label DetectorsCode1
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
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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