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
Promoting Data and Model Privacy in Federated Learning through Quantized LoRA—0
Promptable Anomaly Segmentation with SAM Through Self-Perception Tuning—0
Prompt-Efficient Fine-Tuning for GPT-like Deep Models to Reduce Hallucination and to Improve Reproducibility in Scientific Text Generation Using Stochastic Optimisation Techniques—0
Prompting through Prototype: A Prototype-based Prompt Learning on Pretrained Vision-Language Models—0
Prompt-Tuning SAM: From Generalist to Specialist with only 2048 Parameters and 16 Training Images—0
PROPER: A Progressive Learning Framework for Personalized Large Language Models with Group-Level Adaptation—0
Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities—0
QERA: an Analytical Framework for Quantization Error Reconstruction—0
QFT: Quantized Full-parameter Tuning of LLMs with Affordable Resources—0
Q-PEFT: Query-dependent Parameter Efficient Fine-tuning for Text Reranking with Large Language Models—0
Quantified Task Misalignment to Inform PEFT: An Exploration of Domain Generalization and Catastrophic Forgetting in CLIP—0
Quantum-Enhanced LLM Efficient Fine Tuning—0
QueEn: A Large Language Model for Quechua-English Translation—0
Query-driven Relevant Paragraph Extraction from Legal Judgments—0
R^3Mem: Bridging Memory Retention and Retrieval via Reversible Compression—0
RandLoRA: Full-rank parameter-efficient fine-tuning of large models—0
Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptation—0
RAP: Efficient Text-Video Retrieval with Sparse-and-Correlated Adapter—0
Towards Efficient Vision-Language Tuning: More Information Density, More Generalizability—0
Representation Discrepancy Bridging Method for Remote Sensing Image-Text Retrieval—0
ResLoRA: Identity Residual Mapping in Low-Rank Adaption—0
Resource Allocation for Stable LLM Training in Mobile Edge Computing—0
Resource-Efficient Transfer Learning From Speech Foundation Model Using Hierarchical Feature Fusion—0
Exploring Acoustic Similarity in Emotional Speech and Music via Self-Supervised Representations—0
Revisiting Privacy, Utility, and Efficiency Trade-offs when Fine-Tuning Large Language Models—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