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 676–700 of 935 papers

TitleStatusHype
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