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 501–525 of 935 papers

TitleStatusHype
HARIS: Human-Like Attention for Reference Image Segmentation—0
Harnessing Generative LLMs for Enhanced Financial Event Entity Extraction Performance—0
HD-PiSSA: High-Rank Distributed Orthogonal Adaptation—0
HELENE: Hessian Layer-wise Clipping and Gradient Annealing for Accelerating Fine-tuning LLM with Zeroth-order Optimization—0
HeLM: Highlighted Evidence augmented Language Model for Enhanced Table-to-Text Generation—0
HiFi: High-Information Attention Heads Hold for Parameter-Efficient Model Adaptation—0
HiFi Tuner: High-Fidelity Subject-Driven Fine-Tuning for Diffusion Models—0
High-Accuracy ECG Image Interpretation using Parameter-Efficient LoRA Fine-Tuning with Multimodal LLaMA 3.2—0
HINT: Hypernetwork Instruction Tuning for Efficient Zero- & Few-Shot Generalisation—0
HM3: Heterogeneous Multi-Class Model Merging—0
House of Cards: Massive Weights in LLMs—0
HSACNet: Hierarchical Scale-Aware Consistency Regularized Semi-Supervised Change Detection—0
HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models—0
HUT: A More Computation Efficient Fine-Tuning Method With Hadamard Updated Transformation—0
Hyper Compressed Fine-Tuning of Large Foundation Models with Quantum Inspired Adapters—0
HyperLoader: Integrating Hypernetwork-Based LoRA and Adapter Layers into Multi-Task Transformers for Sequence Labelling—0
Hypernetworks for Personalizing ASR to Atypical Speech—0
HyperPELT: Unified Parameter-Efficient Language Model Tuning for Both Language and Vision-and-Language Tasks—0
HyperTuning: Toward Adapting Large Language Models without Back-propagation—0
IAPT: Instruction-Aware Prompt Tuning for Large Language Models—0
ICL Markup: Structuring In-Context Learning using Soft-Token Tags—0
iConFormer: Dynamic Parameter-Efficient Tuning with Input-Conditioned Adaptation—0
Improving Domain Adaptation through Extended-Text Reading Comprehension—0
Improving Few-shot Generalization of Safety Classifiers via Data Augmented Parameter-Efficient Fine-Tuning—0
Improving LoRA in Privacy-preserving Federated Learning—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