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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 601–650 of 935 papers

TitleStatusHype
DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation—0
Dual Decomposition of Weights and Singular Value Low Rank Adaptation—0
Dual Low-Rank Adaptation for Continual Learning with Pre-Trained Models—0
EEG-Based Mental Imagery Task Adaptation via Ensemble of Weight-Decomposed Low-Rank Adapters—0
Efficiency at Scale: Investigating the Performance of Diminutive Language Models in Clinical Tasks—0
Efficiency in Focus: LayerNorm as a Catalyst for Fine-tuning Medical Visual Language Pre-trained Models—0
Efficient Adaptation For Remote Sensing Visual Grounding—0
Efficient Adaptation of Pre-trained Vision Transformer via Householder Transformation—0
Efficient Adaptation of Pre-trained Vision Transformer underpinned by Approximately Orthogonal Fine-Tuning Strategy—0
Efficient and Effective Adaptation of Multimodal Foundation Models in Sequential Recommendation—0
Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models—0
Efficient Continual Adaptation of Pretrained Robotic Policy with Online Meta-Learned Adapters—0
Efficient Deployment of Large Language Models on Resource-constrained Devices—0
Efficient Differentially Private Fine-Tuning of Diffusion Models—0
Efficient Federated Class-Incremental Learning of Pre-Trained Models via Task-agnostic Low-rank Residual Adaptation—0
Efficient Federated Fine-Tuning of Large Language Models with Layer Dropout—0
Efficient Federated Split Learning for Large Language Models over Communication Networks—0
Efficient In-Domain Question Answering for Resource-Constrained Environments—0
Efficient Telecom Specific LLM: TSLAM-Mini with QLoRA and Digital Twin Data—0
EF-LLM: Energy Forecasting LLM with AI-assisted Automation, Enhanced Sparse Prediction, Hallucination Detection—0
ELBA-Bench: An Efficient Learning Backdoor Attacks Benchmark for Large Language Models—0
ELiTe: Efficient Image-to-LiDAR Knowledge Transfer for Semantic Segmentation—0
Embedding-based statistical inference on generative models—0
Empirical Analysis of the Strengths and Weaknesses of PEFT Techniques for LLMs—0
Enabling Efficient On-Device Fine-Tuning of LLMs Using Only Inference Engines—0
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