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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 601–650 of 935 papers

TitleStatusHype
Cross-Lingual Speech Emotion Recognition: Humans vs. Self-Supervised ModelsCode0
Block Expanded DINORET: Adapting Natural Domain Foundation Models for Retinal Imaging Without Catastrophic Forgetting—0
Enabling Efficient On-Device Fine-Tuning of LLMs Using Only Inference Engines—0
Flat-LoRA: Low-Rank Adaption over a Flat Loss Landscape—0
Obliviate: Neutralizing Task-agnostic Backdoors within the Parameter-efficient Fine-tuning ParadigmCode0
HUT: A More Computation Efficient Fine-Tuning Method With Hadamard Updated Transformation—0
Beyond LoRA: Exploring Efficient Fine-Tuning Techniques for Time Series Foundational Models—0
LPT++: Efficient Training on Mixture of Long-tailed Experts—0
THaMES: An End-to-End Tool for Hallucination Mitigation and Evaluation in Large Language ModelsCode0
From Text to Emoji: How PEFT-Driven Personality Manipulation Unleashes the Emoji Potential in LLMs—0
COMFORT: A Continual Fine-Tuning Framework for Foundation Models Targeted at Consumer Healthcare—0
Risks When Sharing LoRA Fine-Tuned Diffusion Model Weights—0
SVFit: Parameter-Efficient Fine-Tuning of Large Pre-Trained Models Using Singular Values—0
iConFormer: Dynamic Parameter-Efficient Tuning with Input-Conditioned Adaptation—0
Robust Federated Finetuning of Foundation Models via Alternating Minimization of LoRA—0
Deconfounded Causality-aware Parameter-Efficient Fine-Tuning for Problem-Solving Improvement of LLMs—0
User-Specific Dialogue Generation with User Profile-Aware Pre-Training Model and Parameter-Efficient Fine-Tuning—0
A Novel Hybrid Parameter-Efficient Fine-Tuning Approach for Hippocampus Segmentation and Alzheimer's Disease Diagnosis—0
Scaling Up Summarization: Leveraging Large Language Models for Long Text Extractive Summarization—0
FedMCP: Parameter-Efficient Federated Learning with Model-Contrastive Personalization—0
GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMsCode0
Pre-training Everywhere: Parameter-Efficient Fine-Tuning for Medical Image Analysis via Target Parameter Pre-training—0
StyleSpeech: Parameter-efficient Fine Tuning for Pre-trained Controllable Text-to-SpeechCode0
Step-by-Step Unmasking for Parameter-Efficient Fine-tuning of Large Language ModelsCode0
Question answering system of bridge design specification based on large language modelCode0
SAN: Hypothesizing Long-Term Synaptic Development and Neural Engram Mechanism in Scalable Model's Parameter-Efficient Fine-TuningCode0
Advancing Enterprise Spatio-Temporal Forecasting Applications: Data Mining Meets Instruction Tuning of Language Models For Multi-modal Time Series Analysis in Low-Resource Settings—0
Offline Policy Learning via Skill-step Abstraction for Long-horizon Goal-Conditioned Tasks—0
Towards Inducing Document-Level Abilities in Standard Multilingual Neural Machine Translation Models—0
Pluto and Charon: A Time and Memory Efficient Collaborative Edge AI Framework for Personal LLMs Fine-Tuning—0
TeamLoRA: Boosting Low-Rank Adaptation with Expert Collaboration and CompetitionCode0
NoRA: Nested Low-Rank Adaptation for Efficient Fine-Tuning Large Models—0
Combo: Co-speech holistic 3D human motion generation and efficient customizable adaptation in harmony—0
MergeRepair: An Exploratory Study on Merging Task-Specific Adapters in Code LLMs for Automated Program Repair—0
Learning to Route for Dynamic Adapter Composition in Continual Learning with Language Models—0
Adaptive Layer Selection for Efficient Vision Transformer Fine-Tuning—0
A New Chinese Landscape Paintings Generation Model based on Stable Diffusion using DreamBooth—0
KIND: Knowledge Integration and Diversion for Training Decomposable ModelsCode0
LLMI3D: Empowering LLM with 3D Perception from a Single 2D Image—0
Orchid2024: A cultivar-level dataset and methodology for fine-grained classification of Chinese Cymbidium OrchidsCode0
BA-LoRA: Bias-Alleviating Low-Rank Adaptation to Mitigate Catastrophic Inheritance in Large Language ModelsCode0
From Words to Worth: Newborn Article Impact Prediction with LLM—0
Leveraging Parameter Efficient Training Methods for Low Resource Text Classification: A Case Study in Marathi—0
SARA: Singular-Value Based Adaptive Low-Rank Adaption—0
FastEdit: Fast Text-Guided Single-Image Editing via Semantic-Aware Diffusion Fine-Tuning—0
MoDE: Effective Multi-task Parameter Efficient Fine-Tuning with a Mixture of Dyadic Experts—0
Tensor Train Low-rank Approximation (TT-LoRA): Democratizing AI with Accelerated LLMs—0
Correcting Negative Bias in Large Language Models through Negative Attention Score Alignment—0
ELP-Adapters: Parameter Efficient Adapter Tuning for Various Speech Processing Tasks—0
Parameter-Efficient Fine-Tuning via Circular Convolution—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