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 476–500 of 935 papers

TitleStatusHype
FINE: Factorizing Knowledge for Initialization of Variable-sized Diffusion Models—0
Fine-tuning vision foundation model for crack segmentation in civil infrastructures—0
Fine Tuning without Catastrophic Forgetting via Selective Low Rank Adaptation—0
FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications—0
FinSQL: Model-Agnostic LLMs-based Text-to-SQL Framework for Financial Analysis—0
FISH-Tuning: Enhancing PEFT Methods with Fisher Information—0
Flat-LoRA: Low-Rank Adaption over a Flat Loss Landscape—0
FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models—0
From Text to Emoji: How PEFT-Driven Personality Manipulation Unleashes the Emoji Potential in LLMs—0
From Words to Worth: Newborn Article Impact Prediction with LLM—0
Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs—0
G-Adapter: Towards Structure-Aware Parameter-Efficient Transfer Learning for Graph Transformer Networks—0
Gated Low-rank Adaptation for personalized Code-Switching Automatic Speech Recognition on the low-spec devices—0
Systematic Analysis for Pretrained Language Model Priming for Parameter-Efficient Fine-tuning—0
Generalizability of Mixture of Domain-Specific Adapters from the Lens of Signed Weight Directions and its Application to Effective Model Pruning—0
Generalized Tensor-based Parameter-Efficient Fine-Tuning via Lie Group Transformations—0
Generative Modeling of Individual Behavior at Scale—0
GeoLoRA: Geometric integration for parameter efficient fine-tuning—0
Get Large Language Models Ready to Speak: A Late-fusion Approach for Speech Generation—0
GP-MoLFormer: A Foundation Model For Molecular Generation—0
GPT vs RETRO: Exploring the Intersection of Retrieval and Parameter-Efficient Fine-Tuning—0
Graph Adapter of EEG Foundation Models for Parameter Efficient Fine Tuning—0
GraphLoRA: Empowering LLMs Fine-Tuning via Graph Collaboration of MoE—0
GSQ-Tuning: Group-Shared Exponents Integer in Fully Quantized Training for LLMs On-Device Fine-tuning—0
Hallucinations and Truth: A Comprehensive Accuracy Evaluation of RAG, LoRA and DoRA—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