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

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Showing 426–450 of 935 papers

TitleStatusHype
MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts—0
Let's Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large Language Model—0
Activation Control for Efficiently Eliciting Long Chain-of-thought Ability of Language Models—0
Efficient Adaptation of Pre-trained Vision Transformer underpinned by Approximately Orthogonal Fine-Tuning Strategy—0
Lifelong Learning with Task-Specific Adaptation: Addressing the Stability-Plasticity Dilemma—0
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
MAP: Revisiting Weight Decomposition for Low-Rank Adaptation—0
Harnessing Generative LLMs for Enhanced Financial Event Entity Extraction Performance—0
LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning—0
HARIS: Human-Like Attention for Reference Image Segmentation—0
LLMI3D: Empowering LLM with 3D Perception from a Single 2D Image—0
LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient Fine-Tuning—0
Hallucinations and Truth: A Comprehensive Accuracy Evaluation of RAG, LoRA and DoRA—0
LoFT: Low-Rank Adaptation That Behaves Like Full Fine-Tuning—0
Deconfounded Causality-aware Parameter-Efficient Fine-Tuning for Problem-Solving Improvement of LLMs—0
Mamba State-Space Models Are Lyapunov-Stable Learners—0
LoKO: Low-Rank Kalman Optimizer for Online Fine-Tuning of Large Models—0
Efficient Federated Class-Incremental Learning of Pre-Trained Models via Task-agnostic Low-rank Residual Adaptation—0
Adapters Mixup: Mixing Parameter-Efficient Adapters to Enhance the Adversarial Robustness of Fine-tuned Pre-trained Text Classifiers—0
LoRA as a Flexible Framework for Securing Large Vision Systems—0
LoRACode: LoRA Adapters for Code Embeddings—0
GSQ-Tuning: Group-Shared Exponents Integer in Fully Quantized Training for LLMs On-Device Fine-tuning—0
GraphLoRA: Empowering LLMs Fine-Tuning via Graph Collaboration of MoE—0
Decentralized Low-Rank Fine-Tuning of Large Language Models—0
Graph Adapter of EEG Foundation Models for Parameter Efficient Fine Tuning—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