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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 251–300 of 935 papers

TitleStatusHype
ABBA: Highly Expressive Hadamard Product Adaptation for Large Language ModelsCode1
Expanding Sparse Tuning for Low Memory UsageCode1
HALO: Hadamard-Assisted Lower-Precision Optimization for LLMsCode1
CL-LoRA: Continual Low-Rank Adaptation for Rehearsal-Free Class-Incremental LearningCode1
ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot FillerCode1
LoFiT: Localized Fine-tuning on LLM RepresentationsCode1
MapSAM: Adapting Segment Anything Model for Automated Feature Detection in Historical MapsCode1
PACE: Marrying generalization in PArameter-efficient fine-tuning with Consistency rEgularizationCode1
FineDiffusion: Scaling up Diffusion Models for Fine-grained Image Generation with 10,000 ClassesCode1
Imaging foundation model for universal enhancement of non-ideal measurement CTCode1
Exploring Foundation Models Fine-Tuning for Cytology ClassificationCode1
Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language ModelsCode1
Exact and Efficient Unlearning for Large Language Model-based Recommendation—0
Choice of PEFT Technique in Continual Learning: Prompt Tuning is Not All You Need—0
Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning—0
An Empirical Study on the Transferability of Transformer Modules in Parameter-Efficient Fine-Tuning—0
HyperLoader: Integrating Hypernetwork-Based LoRA and Adapter Layers into Multi-Task Transformers for Sequence Labelling—0
Enhancing News Summarization with ELearnFit through Efficient In-Context Learning and Efficient Fine-Tuning—0
Enhancing Multi-modal Models with Heterogeneous MoE Adapters for Fine-tuning—0
Challenges in Adapting Multilingual LLMs to Low-Resource Languages using LoRA PEFT Tuning—0
Enhancing Multilingual Speech Recognition through Language Prompt Tuning and Frame-Level Language Adapter—0
Enhancing Low-Resource LLMs Classification with PEFT and Synthetic Data—0
Chain of LoRA: Efficient Fine-tuning of Language Models via Residual Learning—0
Hypernetworks for Personalizing ASR to Atypical Speech—0
Enhancing Large Language Model Efficiencyvia Symbolic Compression: A Formal Approach Towards Interpretability—0
Enhancing knowledge retention for continual learning with domain-specific adapters and features gating—0
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
HyperPELT: Unified Parameter-Efficient Language Model Tuning for Both Language and Vision-and-Language Tasks—0
HUT: A More Computation Efficient Fine-Tuning Method With Hadamard Updated Transformation—0
Empirical Analysis of the Strengths and Weaknesses of PEFT Techniques for LLMs—0
Embedding-based statistical inference on generative models—0
ELiTe: Efficient Image-to-LiDAR Knowledge Transfer for Semantic Segmentation—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
Hyper Compressed Fine-Tuning of Large Foundation Models with Quantum Inspired Adapters—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
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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