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
Enfoque Odychess: Un método dialéctico, constructivista y adaptativo para la enseñanza del ajedrez con inteligencias artificiales generativas—0
Enhanced Continual Learning of Vision-Language Models with Model Fusion—0
Enhancing Aviation Communication Transcription: Fine-Tuning Distil-Whisper with LoRA—0
Enhancing knowledge retention for continual learning with domain-specific adapters and features gating—0
Enhancing Large Language Model Efficiencyvia Symbolic Compression: A Formal Approach Towards Interpretability—0
Enhancing Low-Resource LLMs Classification with PEFT and Synthetic Data—0
Enhancing Multilingual Speech Recognition through Language Prompt Tuning and Frame-Level Language Adapter—0
Enhancing Multi-modal Models with Heterogeneous MoE Adapters for Fine-tuning—0
Enhancing News Summarization with ELearnFit through Efficient In-Context Learning and Efficient Fine-Tuning—0
Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning—0
Exact and Efficient Unlearning for Large Language Model-based Recommendation—0
Explainable ICD Coding via Entity Linking—0
Explain Less, Understand More: Jargon Detection via Personalized Parameter-Efficient Fine-tuning—0
ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts—0
Exploring Adapter Design Tradeoffs for Low Resource Music Generation—0
Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning—0
Exploring Zero and Few-shot Techniques for Intent Classification—0
External Prompt Features Enhanced Parameter-efficient Fine-tuning for Salient Object Detection—0
F^3OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics—0
F^3OCUS - Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics—0
FairLoRA: Unpacking Bias Mitigation in Vision Models with Fairness-Driven Low-Rank Adaptation—0
FastEdit: Fast Text-Guided Single-Image Editing via Semantic-Aware Diffusion Fine-Tuning—0
Fast-NTK: Parameter-Efficient Unlearning for Large-Scale Models—0
FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition—0
Federated Adapter on Foundation Models: An Out-Of-Distribution Approach—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