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 776–800 of 935 papers

TitleStatusHype
FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications—0
Let's Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large Language Model—0
Empirical Studies of Parameter Efficient Methods for Large Language Models of Code and Knowledge Transfer to RCode0
Introducing Routing Functions to Vision-Language Parameter-Efficient Fine-Tuning with Low-Rank BottlenecksCode0
An Empirical Study of Parameter Efficient Fine-tuning on Vision-Language Pre-train Model—0
Targeted Efficient Fine-tuning: Optimizing Parameter Updates with Data-Driven Sample Selection—0
Matrix-Transformation Based Low-Rank Adaptation (MTLoRA): A Brain-Inspired Method for Parameter-Efficient Fine-Tuning—0
Block-wise LoRA: Revisiting Fine-grained LoRA for Effective Personalization and Stylization in Text-to-Image Generation—0
RIFF: Learning to Rephrase Inputs for Few-shot Fine-tuning of Language ModelsCode0
STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language ModelsCode0
ResLoRA: Identity Residual Mapping in Low-Rank Adaption—0
Inducing Generalization across Languages and Tasks using Featurized Low-Rank Mixtures—0
DiffuseKronA: A Parameter Efficient Fine-tuning Method for Personalized Diffusion Models—0
A Fine-tuning Enhanced RAG System with Quantized Influence Measure as AI Judge—0
MIP: CLIP-based Image Reconstruction from PEFT Gradients—0
PeriodicLoRA: Breaking the Low-Rank Bottleneck in LoRA Optimization—0
Sparse MeZO: Less Parameters for Better Performance in Zeroth-Order LLM Fine-Tuning—0
Does Combining Parameter-efficient Modules Improve Few-shot Transfer Accuracy?—0
PEMT: Multi-Task Correlation Guided Mixture-of-Experts Enables Parameter-Efficient Transfer Learning—0
Two-stage Cytopathological Image Synthesis for Augmenting Cervical Abnormality Screening—0
MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models—0
NOTE: Notable generation Of patient Text summaries through Efficient approach based on direct preference optimization—0
Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning—0
Bayesian Parameter-Efficient Fine-Tuning for Overcoming Catastrophic ForgettingCode0
LoRA Training in the NTK Regime has No Spurious Local MinimaCode0
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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