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 626–650 of 935 papers

TitleStatusHype
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