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 551–575 of 935 papers

TitleStatusHype
Towards Real Zero-Shot Camouflaged Object Segmentation without Camouflaged AnnotationsCode0
The effect of fine-tuning on language model toxicityCode0
Benchmarking Pathology Foundation Models: Adaptation Strategies and ScenariosCode0
MIRA: A Method of Federated MultI-Task Learning for LaRge LAnguage Models—0
Unlearning Backdoor Attacks for LLMs with Weak-to-Strong Knowledge DistillationCode0
Parameter-efficient Adaptation of Multilingual Multimodal Models for Low-resource ASR—0
LoLDU: Low-Rank Adaptation via Lower-Diag-Upper Decomposition for Parameter-Efficient Fine-TuningCode0
Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models—0
Sequential LLM Framework for Fashion Recommendation—0
LoKO: Low-Rank Kalman Optimizer for Online Fine-Tuning of Large Models—0
Layer-wise Importance Matters: Less Memory for Better Performance in Parameter-efficient Fine-tuning of Large Language ModelsCode0
RoCoFT: Efficient Finetuning of Large Language Models with Row-Column UpdatesCode0
BiDoRA: Bi-level Optimization-Based Weight-Decomposed Low-Rank Adaptation—0
DARE the Extreme: Revisiting Delta-Parameter Pruning For Fine-Tuned ModelsCode0
Parameter-Efficient Fine-Tuning of Large Language Models using Semantic Knowledge Tuning—0
QEFT: Quantization for Efficient Fine-Tuning of LLMsCode0
SLIM: Let LLM Learn More and Forget Less with Soft LoRA and Identity Mixture—0
ACCEPT: Adaptive Codebook for Composite and Efficient Prompt TuningCode0
MotionAura: Generating High-Quality and Motion Consistent Videos using Discrete DiffusionCode0
Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptation—0
Parameter-Efficient Fine-Tuning via Selective Discrete Cosine Transform—0
Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs—0
SparseGrad: A Selective Method for Efficient Fine-tuning of MLP LayersCode0
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
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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