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 401–425 of 935 papers

TitleStatusHype
Efficient In-Domain Question Answering for Resource-Constrained Environments—0
PEDRO: Parameter-Efficient Fine-tuning with Prompt DEpenDent Representation MOdification—0
Weak-to-Strong Backdoor Attack for Large Language Models—0
Exploring Acoustic Similarity in Emotional Speech and Music via Self-Supervised Representations—0
Multi-View and Multi-Scale Alignment for Contrastive Language-Image Pre-training in MammographyCode0
Block Expanded DINORET: Adapting Natural Domain Foundation Models for Retinal Imaging Without Catastrophic Forgetting—0
PACE: Marrying generalization in PArameter-efficient fine-tuning with Consistency rEgularizationCode1
Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation—0
Cross-Lingual Speech Emotion Recognition: Humans vs. Self-Supervised ModelsCode0
Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual RecognitionCode1
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
Balancing LoRA Performance and Efficiency with Simple Shard SharingCode2
Propulsion: Steering LLM with Tiny Fine-TuningCode1
Beyond LoRA: Exploring Efficient Fine-Tuning Techniques for Time Series Foundational Models—0
THaMES: An End-to-End Tool for Hallucination Mitigation and Evaluation in Large Language ModelsCode0
LPT++: Efficient Training on Mixture of Long-tailed Experts—0
From Text to Emoji: How PEFT-Driven Personality Manipulation Unleashes the Emoji Potential in LLMs—0
Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language ModelsCode2
COMFORT: A Continual Fine-Tuning Framework for Foundation Models Targeted at Consumer Healthcare—0
Risks When Sharing LoRA Fine-Tuned Diffusion Model Weights—0
Do Vision Foundation Models Enhance Domain Generalization in Medical Image Segmentation?Code1
Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA RegionCode1
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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