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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 851–900 of 935 papers

TitleStatusHype
Permissioned LLMs: Enforcing Access Control in Large Language Models—0
Personalized Federated Fine-tuning for Heterogeneous Data: An Automatic Rank Learning Approach via Two-Level LoRA—0
Personalized Text Generation with Contrastive Activation Steering—0
PETALface: Parameter Efficient Transfer Learning for Low-resolution Face Recognition—0
PETapter: Leveraging PET-style classification heads for modular few-shot parameter-efficient fine-tuning—0
pFedMxF: Personalized Federated Class-Incremental Learning with Mixture of Frequency Aggregation—0
Pluto and Charon: A Time and Memory Efficient Collaborative Edge AI Framework for Personal LLMs Fine-Tuning—0
Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks—0
PortLLM: Personalizing Evolving Large Language Models with Training-Free and Portable Model Patches—0
Position-Aware Parameter Efficient Fine-Tuning Approach for Reducing Positional Bias in LLMs—0
Prefix-Tuning+: Modernizing Prefix-Tuning by Decoupling the Prefix from Attention—0
PreQuant: A Task-agnostic Quantization Approach for Pre-trained Language Models—0
Pre-Trained Vision-Language Models as Partial Annotators—0
Pre-training Everywhere: Parameter-Efficient Fine-Tuning for Medical Image Analysis via Target Parameter Pre-training—0
PRILoRA: Pruned and Rank-Increasing Low-Rank Adaptation—0
Understanding and Improving Transfer Learning of Deep Models via Neural Collapse—0
Privacy Preserving Conversion Modeling in Data Clean Room—0
Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for Medical Image Classification—0
Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning—0
Progtuning: Progressive Fine-tuning Framework for Transformer-based Language Models—0
Project-Probe-Aggregate: Efficient Fine-Tuning for Group Robustness—0
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
SwitchLoRA: Switched Low-Rank Adaptation Can Learn Full-Rank Information—0
Risks When Sharing LoRA Fine-Tuned Diffusion Model Weights—0
Robust Federated Finetuning of Foundation Models via Alternating Minimization of LoRA—0
Robust Federated Finetuning of LLMs via Alternating Optimization of LoRA—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