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 726–750 of 935 papers

TitleStatusHype
Selective Fine-tuning on LLM-labeled Data May Reduce Reliance on Human Annotation: A Case Study Using Schedule-of-Event Table Detection—0
Parameter-Efficient Fine-Tuning With Adapters—0
CourseGPT-zh: an Educational Large Language Model Based on Knowledge Distillation Incorporating Prompt Optimization—0
Refining Joint Text and Source Code Embeddings for Retrieval Task with Parameter-Efficient Fine-TuningCode0
ELiTe: Efficient Image-to-LiDAR Knowledge Transfer for Semantic Segmentation—0
Enhancing News Summarization with ELearnFit through Efficient In-Context Learning and Efficient Fine-Tuning—0
TartuNLP at EvaLatin 2024: Emotion Polarity Detection—0
Investigating Automatic Scoring and Feedback using Large Language Models—0
MoPEFT: A Mixture-of-PEFTs for the Segment Anything Model—0
RST-LoRA: A Discourse-Aware Low-Rank Adaptation for Long Document Abstractive Summarization—0
SPAFIT: Stratified Progressive Adaptation Fine-tuning for Pre-trained Large Language Models—0
FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition—0
Parameter-Efficient Tuning Large Language Models for Graph Representation Learning—0
Efficiency in Focus: LayerNorm as a Catalyst for Fine-tuning Medical Visual Language Pre-trained Models—0
Gated Low-rank Adaptation for personalized Code-Switching Automatic Speech Recognition on the low-spec devices—0
External Prompt Features Enhanced Parameter-efficient Fine-tuning for Salient Object Detection—0
ColA: Collaborative Adaptation with Gradient LearningCode0
Parameter Efficient Fine Tuning: A Comprehensive Analysis Across Applications—0
iTBLS: A Dataset of Interactive Conversations Over Tabular Information—0
Mixed Text Recognition with Efficient Parameter Fine-Tuning and Transformer—0
TartuNLP @ SIGTYP 2024 Shared Task: Adapting XLM-RoBERTa for Ancient and Historical Languages—0
Skeleton: A New Framework for Accelerating Language Models via Task Neuron Localized Prompt Tuning—0
Exact and Efficient Unlearning for Large Language Model-based Recommendation—0
Shears: Unstructured Sparsity with Neural Low-rank Adapter Search—0
LoRA Dropout as a Sparsity Regularizer for Overfitting Control—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