← Learning Types
Deep Learning › Learning Types ›

Parameter-Efficient Fine-Tuning

33 directly classified papers

Papers per year

Papers

FT-MDT: Extracting Decision Trees from Medical Texts via a Novel Low-rank Adaptation Method EMNLP 2025
ProMALex: Progressive Modular Adapters for Multi-Jurisdictional Legal Language Modeling ACL 2025
In-Context Meta LoRA Generation IJCAI 2025
LoKi: Low-dimensional KAN for Efficient Fine-tuning Image Models CVPR 2025
SPMTrack: Spatio-Temporal Parameter-Efficient Fine-Tuning with Mixture of Experts for Scalable Visual Tracking CVPR 2025
MobiZO: Enabling Efficient LLM Fine-Tuning at the Edge via Inference Engines EMNLP 2025
Parameter-Efficient Fine-Tuning via Circular Convolution ACL 2025
Alleviating Hallucinations in Large Language Models via Truthfulness-driven Rank-adaptive LoRA ACL 2025
R3Mem: Bridging Memory Retention and Retrieval via Reversible Compression ACL 2025
SuLoRA: Subspace Low-Rank Adaptation for Parameter-Efficient Fine-Tuning ACL 2025
GinGer at SemEval-2025 Task 11: Leveraging Fine-Tuned Transformer Models and LoRA for Sentiment Analysis in Low-Resource Languages ACL 2025
SA²VP: Spatially Aligned-and-Adapted Visual Prompt AAAI 2024
Point-PEFT: Parameter-Efficient Fine-Tuning for 3D Pre-trained Models AAAI 2024
LoRA-Flow: Dynamic LoRA Fusion for Large Language Models in Generative Tasks ACL 2024
Light-PEFT: Lightening Parameter-Efficient Fine-Tuning via Early Pruning ACL 2024
RIFF: Learning to Rephrase Inputs for Few-shot Fine-tuning of Language Models ACL 2024
FanLoRA: Fantastic LoRAs and Where to Find Them in Large Language Model Fine-tuning EMNLP 2024
Instance-Level Dynamic LoRAs Composition for Cross-Task Generalization EMNLP 2024
SQFT: Low-cost Model Adaptation in Low-precision Sparse Foundation Models EMNLP 2024
Visual Perception by Large Language Model’s Weights NIPS 2024
Prompt Tuning Strikes Back: Customizing Foundation Models with Low-Rank Prompt Adaptation NIPS 2024
$\textit{Trans-LoRA}$: towards data-free Transferable Parameter Efficient Finetuning NIPS 2024
PACE: Marrying generalization in PArameter-efficient fine-tuning with Consistency rEgularization NIPS 2024
Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning EMNLP 2024
Exploring Intrinsic Language-specific Subspaces in Fine-tuning Multilingual Neural Machine Translation EMNLP 2024