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 351–375 of 935 papers

TitleStatusHype
Does Combining Parameter-efficient Modules Improve Few-shot Transfer Accuracy?—0
Bayesian-LoRA: LoRA based Parameter Efficient Fine-Tuning using Optimal Quantization levels and Rank Values trough Differentiable Bayesian Gates—0
Lifelong Learning with Task-Specific Adaptation: Addressing the Stability-Plasticity Dilemma—0
Balancing Stability and Plasticity in Pretrained Detector: A Dual-Path Framework for Incremental Object Detection—0
Mixed Text Recognition with Efficient Parameter Fine-Tuning and Transformer—0
A GEN AI Framework for Medical Note Generation—0
DLoRA: Distributed Parameter-Efficient Fine-Tuning Solution for Large Language Model—0
HyperLoader: Integrating Hypernetwork-Based LoRA and Adapter Layers into Multi-Task Transformers for Sequence Labelling—0
AutoPsyC: Automatic Recognition of Psychodynamic Conflicts from Semi-structured Interviews with Large Language Models—0
Hyper Compressed Fine-Tuning of Large Foundation Models with Quantum Inspired Adapters—0
DiffuseKronA: A Parameter Efficient Fine-tuning Method for Personalized Diffusion Models—0
DiffoRA: Enabling Parameter-Efficient LLM Fine-Tuning via Differential Low-Rank Matrix Adaptation—0
AFLoRA: Adaptive Freezing of Low Rank Adaptation in Parameter Efficient Fine-Tuning of Large Models—0
HUT: A More Computation Efficient Fine-Tuning Method With Hadamard Updated Transformation—0
HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models—0
HSACNet: Hierarchical Scale-Aware Consistency Regularized Semi-Supervised Change Detection—0
Differentially Private Fine-Tuning of Diffusion Models—0
AdaFish: Fast low-rank parameter-efficient fine-tuning by using second-order information—0
House of Cards: Massive Weights in LLMs—0
HM3: Heterogeneous Multi-Class Model Merging—0
DiDOTS: Knowledge Distillation from Large-Language-Models for Dementia Obfuscation in Transcribed Speech—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
HINT: Hypernetwork Instruction Tuning for Efficient Zero- & Few-Shot Generalisation—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