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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 276–300 of 935 papers

TitleStatusHype
Chain of History: Learning and Forecasting with LLMs for Temporal Knowledge Graph Completion—0
Enhancing Aviation Communication Transcription: Fine-Tuning Distil-Whisper with LoRA—0
Enhanced Continual Learning of Vision-Language Models with Model Fusion—0
Certified PEFTSmoothing: Parameter-Efficient Fine-Tuning with Randomized Smoothing—0
An Empirical Study of Parameter Efficient Fine-tuning on Vision-Language Pre-train Model—0
Enfoque Odychess: Un método dialéctico, constructivista y adaptativo para la enseñanza del ajedrez con inteligencias artificiales generativas—0
Enabling Efficient On-Device Fine-Tuning of LLMs Using Only Inference Engines—0
CatMemo at the FinLLM Challenge Task: Fine-Tuning Large Language Models using Data Fusion in Financial Applications—0
CASA: Class-Agnostic Shared Attributes in Vision-Language Models for Efficient Incremental Object Detection—0
6G WavesFM: A Foundation Model for Sensing, Communication, and Localization—0
Empirical Analysis of the Strengths and Weaknesses of PEFT Techniques for LLMs—0
HM3: Heterogeneous Multi-Class Model Merging—0
Embedding-based statistical inference on generative models—0
ELiTe: Efficient Image-to-LiDAR Knowledge Transfer for Semantic Segmentation—0
House of Cards: Massive Weights in LLMs—0
ELBA-Bench: An Efficient Learning Backdoor Attacks Benchmark for Large Language Models—0
EF-LLM: Energy Forecasting LLM with AI-assisted Automation, Enhanced Sparse Prediction, Hallucination Detection—0
Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation—0
Efficient Telecom Specific LLM: TSLAM-Mini with QLoRA and Digital Twin Data—0
BoRA: Bi-dimensional Weight-Decomposed Low-Rank Adaptation—0
Adapting Segment Anything Model to Melanoma Segmentation in Microscopy Slide Images—0
HSACNet: Hierarchical Scale-Aware Consistency Regularized Semi-Supervised Change Detection—0
A Multi-Encoder Frozen-Decoder Approach for Fine-Tuning Large Language Models—0
MoDE: Effective Multi-task Parameter Efficient Fine-Tuning with a Mixture of Dyadic Experts—0
High-Accuracy ECG Image Interpretation using Parameter-Efficient LoRA Fine-Tuning with Multimodal LLaMA 3.2—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