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

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Showing 576–600 of 935 papers

TitleStatusHype
Combo: Co-speech holistic 3D human motion generation and efficient customizable adaptation in harmony—0
COMFORT: A Continual Fine-Tuning Framework for Foundation Models Targeted at Consumer Healthcare—0
Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models—0
Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification—0
Context-PEFT: Efficient Multi-Modal, Multi-Task Fine-Tuning—0
Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editing—0
Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model—0
CourseGPT-zh: an Educational Large Language Model Based on Knowledge Distillation Incorporating Prompt Optimization—0
CPP-UT-Bench: Can LLMs Write Complex Unit Tests in C++?—0
CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices—0
CULL-MT: Compression Using Language and Layer pruning for Machine Translation—0
Customizable Combination of Parameter-Efficient Modules for Multi-Task Learning—0
DAPE: Dual-Stage Parameter-Efficient Fine-Tuning for Consistent Video Editing with Diffusion Models—0
Targeted Efficient Fine-tuning: Optimizing Parameter Updates with Data-Driven Sample Selection—0
Decentralized Low-Rank Fine-Tuning of Large Language Models—0
Deconfounded Causality-aware Parameter-Efficient Fine-Tuning for Problem-Solving Improvement of LLMs—0
Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning—0
DESIRE: Dynamic Knowledge Consolidation for Rehearsal-Free Continual Learning—0
DiDOTS: Knowledge Distillation from Large-Language-Models for Dementia Obfuscation in Transcribed Speech—0
Differentially Private Fine-Tuning of Diffusion Models—0
DiffoRA: Enabling Parameter-Efficient LLM Fine-Tuning via Differential Low-Rank Matrix Adaptation—0
DiffuseKronA: A Parameter Efficient Fine-tuning Method for Personalized Diffusion Models—0
DLoRA: Distributed Parameter-Efficient Fine-Tuning Solution for Large Language Model—0
Mixed Text Recognition with Efficient Parameter Fine-Tuning and Transformer—0
Does Combining Parameter-efficient Modules Improve Few-shot Transfer Accuracy?—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