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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 901–935 of 935 papers

TitleStatusHype
LACoS-BLOOM: Low-rank Adaptation with Contrastive objective on 8 bits Siamese-BLOOM—0
Text-guided High-definition Consistency Texture Model—0
HiFi: High-Information Attention Heads Hold for Parameter-Efficient Model Adaptation—0
RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language ModelsCode0
Parameter-Efficient Cross-lingual Transfer of Vision and Language Models via Translation-based AlignmentCode0
Empirical Analysis of the Strengths and Weaknesses of PEFT Techniques for LLMs—0
AMR Parsing with Instruction Fine-tuned Pre-trained Language Models—0
Strong Baselines for Parameter Efficient Few-Shot Fine-tuning—0
Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-TuningCode0
RCA: Region Conditioned Adaptation for Visual Abductive ReasoningCode0
An Empirical Study on the Transferability of Transformer Modules in Parameter-Efficient Fine-Tuning—0
Parameter-Efficient Fine-Tuning Design Spaces—0
Adapting Shortcut With Normalizing Flow: An Efficient Tuning Framework for Visual RecognitionCode0
Understanding and Improving Transfer Learning of Deep Models via Neural Collapse—0
SPT: Semi-Parametric Prompt Tuning for Multitask Prompted Learning—0
HINT: Hypernetwork Instruction Tuning for Efficient Zero- & Few-Shot Generalisation—0
Parameter-Efficient Finetuning of Transformers for Source CodeCode0
Systematic Analysis for Pretrained Language Model Priming for Parameter-Efficient Fine-tuning—0
HyperTuning: Toward Adapting Large Language Models without Back-propagation—0
Multi-Head Adapter Routing for Cross-Task Generalization—0
Resource-Efficient Transfer Learning From Speech Foundation Model Using Hierarchical Feature Fusion—0
Adapter-Based Extension of Multi-Speaker Text-to-Speech Model for New Speakers—0
Prompting through Prototype: A Prototype-based Prompt Learning on Pretrained Vision-Language Models—0
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models—0
Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks—0
Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning—0
Meta-Adapters: Parameter Efficient Few-shot Fine-tuning through Meta-LearningCode0
Know Where You're Going: Meta-Learning for Parameter-Efficient Fine-Tuning—0
When does Parameter-Efficient Transfer Learning Work for Machine Translation?Code0
CLIP Models are Few-shot Learners: Empirical Studies on VQA and Visual Entailment—0
HyperPELT: Unified Parameter-Efficient Language Model Tuning for Both Language and Vision-and-Language Tasks—0
Meta-Adapter: Parameter Efficient Few-Shot Learning through Meta-Learning—0
DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language ModelsCode0
BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models—0
Style Attuned Pre-training and Parameter Efficient Fine-tuning for Spoken Language Understanding—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