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 851–875 of 935 papers

TitleStatusHype
On the Analysis of Cross-Lingual Prompt Tuning for Decoder-based Multilingual Model—0
Low-Rank Adaptation for Multilingual Summarization: An Empirical Study—0
PEMA: An Offsite-Tunable Plug-in External Memory Adaptation for Language ModelsCode0
Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse FinetuningCode0
BioInstruct: Instruction Tuning of Large Language Models for Biomedical Natural Language Processing—0
FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation Models with Mobile Edge Computing—0
Improving Few-shot Generalization of Safety Classifiers via Data Augmented Parameter-Efficient Fine-Tuning—0
Mixture-of-Linguistic-Experts Adapters for Improving and Interpreting Pre-trained Language Models—0
Improving generalization in large language models by learning prefix subspacesCode0
Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editing—0
Identifying and Adapting Transformer-Components Responsible for Gender Bias in an English Language ModelCode0
Non-Intrusive Adaptation: Input-Centric Parameter-efficient Fine-Tuning for Versatile Multimodal Modeling—0
Prototype-based HyperAdapter for Sample-Efficient Multi-task TuningCode0
QFT: Quantized Full-parameter Tuning of LLMs with Affordable Resources—0
TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models—0
Parameterizing Context: Unleashing the Power of Parameter-Efficient Fine-Tuning and In-Context Tuning for Continual Table Semantic Parsing—0
Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning—0
Conversational Factor Information Retrieval Model (ConFIRM)Code0
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
PEFTT: Parameter-Efficient Fine-Tuning for low-resource Tibetan pre-trained language models—0
Sparsely Shared LoRA on Whisper for Child Speech Recognition—0
Test-Time Training for Speech—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