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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 801–850 of 935 papers

TitleStatusHype
QFT: Quantized Full-parameter Tuning of LLMs with Affordable Resources—0
TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models—0
FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image AnalysisCode1
Parameterizing Context: Unleashing the Power of Parameter-Efficient Fine-Tuning and In-Context Tuning for Continual Table Semantic Parsing—0
Parameter Efficient Multi-task Model Fusion with Partial LinearizationCode1
Conversational Factor Information Retrieval Model (ConFIRM)Code0
Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning—0
EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion ModelsCode1
Point-PEFT: Parameter-Efficient Fine-Tuning for 3D Pre-trained ModelsCode1
Empirical Study of PEFT techniques for Winter Wheat SegmentationCode1
DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion ModelsCode1
PETA: Parameter-Efficient Trojan Attacks—0
LoRA ensembles for large language model fine-tuning—0
Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities—0
LORD: Low Rank Decomposition Of Monolingual Code LLMs For One-Shot Compression—0
MediViSTA: Medical Video Segmentation via Temporal Fusion SAM Adaptation for EchocardiographyCode1
PEFTT: Parameter-Efficient Fine-Tuning for low-resource Tibetan pre-trained language models—0
LongLoRA: Efficient Fine-tuning of Long-Context Large Language ModelsCode6
Sparsely Shared LoRA on Whisper for Child Speech Recognition—0
LLM-based Medical Assistant Personalization with Short- and Long-Term Memory CoordinationCode1
Test-Time Training for Speech—0
Enhancing Multilingual Speech Recognition through Language Prompt Tuning and Frame-Level Language Adapter—0
MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image SegmentationCode1
FedJudge: Federated Legal Large Language ModelCode1
SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient ChannelsCode1
Hydra: Multi-head Low-rank Adaptation for Parameter Efficient Fine-tuningCode1
Scaled Prompt-Tuning for Few-Shot Natural Language Generation—0
Exploring the Benefits of Differentially Private Pre-training and Parameter-Efficient Fine-tuning for Table TransformersCode0
DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuningCode1
Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction TuningCode2
FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning—0
Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction FollowingCode2
SAM-PARSER: Fine-tuning SAM Efficiently by Parameter Space Reconstruction—0
SoTaNa: The Open-Source Software Development AssistantCode1
IncreLoRA: Incremental Parameter Allocation Method for Parameter-Efficient Fine-tuningCode1
LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning—0
Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language ModelsCode1
Comparison between parameter-efficient techniques and full fine-tuning: A case study on multilingual news article classificationCode0
SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models—0
Towards Instance-adaptive Inference for Federated LearningCode1
WIKITIDE: A Wikipedia-Based Timestamped Definition Pairs Dataset—0
SimTeG: A Frustratingly Simple Approach Improves Textual Graph LearningCode1
MA-FSAR: Multimodal Adaptation of CLIP for Few-Shot Action Recognition—0
Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectives—0
DVPT: Dynamic Visual Prompt Tuning of Large Pre-trained Models for Medical Image AnalysisCode0
SuryaKiran at MEDIQA-Sum 2023: Leveraging LoRA for Clinical Dialogue Summarization—0
Parameter-Efficient Fine-Tuning of LLaMA for the Clinical DomainCode1
RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote SensingCode2
Full Parameter Fine-tuning for Large Language Models with Limited ResourcesCode2
One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuningCode2
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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