Imagen de Portada

Origin Tracer: A Method for Detecting LoRA Fine-Tuning Origins in LLMs

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Liang, Hongyu, Zheng, Yuting, Li, Yihan, Zhang, Yiran, Liang, Shiyu
Formato:	Preprint
Publicado:	2025
Materias:	Artificial Intelligence Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

LoRAFusion: Efficient LoRA Fine-Tuning for LLMs
por: Zhu, Zhanda, et al.
Publicado: (2025)

Rethinking the Rank Threshold for LoRA Fine-Tuning
por: Park, Juneyoung
Publicado: (2026)

Activated LoRA: Fine-tuned LLMs for Intrinsics
por: Greenewald, Kristjan, et al.
Publicado: (2025)

KD-LoRA: A Hybrid Approach to Efficient Fine-Tuning with LoRA and Knowledge Distillation
por: Azimi, Rambod, et al.
Publicado: (2024)

FedTreeLoRA: Reconciling Statistical and Functional Heterogeneity in Federated LoRA Fine-Tuning
por: Bian, Jieming, et al.
Publicado: (2026)

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization
por: Deng, Yanxia, et al.
Publicado: (2025)

On-the-Fly Adaptation to Quantization: Configuration-Aware LoRA for Efficient Fine-Tuning of Quantized LLMs
por: Ye, Rongguang, et al.
Publicado: (2025)

mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs
por: Ye, Zhengmao, et al.
Publicado: (2023)

FedMomentum: Preserving LoRA Training Momentum in Federated Fine-Tuning
por: Yan, Peishen, et al.
Publicado: (2026)

Kron-LoRA: Hybrid Kronecker-LoRA Adapters for Scalable, Sustainable Fine-tuning
por: Shen, Yixin
Publicado: (2025)

A Sensitivity-Driven Expert Allocation Method in LoRA-MoE for Efficient Fine-Tuning
por: Xu, Junzhou, et al.
Publicado: (2025)

Parameter-Efficient Fine-Tuning for HAR: Integrating LoRA and QLoRA into Transformer Models
por: Seregina, Irina, et al.
Publicado: (2025)

Convergence Analysis of Aggregation-Broadcast in LoRA-enabled Distributed Fine-Tuning
por: Chen, Xin, et al.
Publicado: (2025)

$α$-LoRA: Effective Fine-Tuning via Base Model Rescaling
por: Firdoussi, Aymane El, et al.
Publicado: (2025)

SC-LoRA: Balancing Efficient Fine-tuning and Knowledge Preservation via Subspace-Constrained LoRA
por: Luo, Minrui, et al.
Publicado: (2025)

Hybrid-LoRA: Bridging Full Fine-Tuning and Low-Rank Adaptation for Post-Training
por: Zhang, Chengqian, et al.
Publicado: (2026)

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs
por: Arabpour, Reza, et al.
Publicado: (2025)

Bi-LoRA: Efficient Sharpness-Aware Minimization for Fine-Tuning Large-Scale Models
por: Liu, Yuhang, et al.
Publicado: (2025)

LoBAM: LoRA-Based Backdoor Attack on Model Merging
por: Yin, Ming, et al.
Publicado: (2024)

LoRA-Mixer: Coordinate Modular LoRA Experts Through Serial Attention Routing
por: Li, Wenbing, et al.
Publicado: (2025)

Neurosymbolic LoRA: Why and When to Tune Weights vs. Rewrite Prompts
por: Wang, Kevin, et al.
Publicado: (2026)

Wireless Federated Multi-Task LLM Fine-Tuning via Sparse-and-Orthogonal LoRA
por: Yang, Nuocheng, et al.
Publicado: (2026)

Echo-LoRA: Parameter-Efficient Fine-Tuning via Cross-Layer Representation Injection
por: Peng, Yihang, et al.
Publicado: (2026)

ALTO: Adaptive LoRA Tuning and Orchestration for Heterogeneous LoRA Training Workloads
por: Zuo, Jingwei, et al.
Publicado: (2026)

SCALE-LoRA: Auditing Post-Retrieval LoRA Composition with Residual Merging and View Reliability
por: Zhou, Shuaipeng, et al.
Publicado: (2026)

Profiling LoRA/QLoRA Fine-Tuning Efficiency on Consumer GPUs: An RTX 4060 Case Study
por: Avinash, MSR
Publicado: (2025)

Localized LoRA: A Structured Low-Rank Approximation for Efficient Fine-Tuning
por: Barazandeh, Babak, et al.
Publicado: (2025)

S-LoRA: Serving Thousands of Concurrent LoRA Adapters
por: Sheng, Ying, et al.
Publicado: (2023)

TT-LoRA MoE: Unifying Parameter-Efficient Fine-Tuning and Sparse Mixture-of-Experts
por: Kunwar, Pradip, et al.
Publicado: (2025)

Computational Limits of Low-Rank Adaptation (LoRA) Fine-Tuning for Transformer Models
por: Hu, Jerry Yao-Chieh, et al.
Publicado: (2024)

MiCA Learns More Knowledge Than LoRA and Full Fine-Tuning
por: Rüdiger, Sten, et al.
Publicado: (2026)

LoRALib: A Standardized Benchmark for Evaluating LoRA-MoE Methods
por: Wang, Shaoheng, et al.
Publicado: (2025)

R-LoRA: Randomized Multi-Head LoRA for Efficient Multi-Task Learning
por: Liu, Jinda, et al.
Publicado: (2025)

ST-LoRA: Low-rank Adaptation for Spatio-Temporal Forecasting
por: Ruan, Weilin, et al.
Publicado: (2024)

FedALT: Federated Fine-Tuning through Adaptive Local Training with Rest-of-World LoRA
por: Bian, Jieming, et al.
Publicado: (2025)

Exploring Gradient Subspaces: Addressing and Overcoming LoRA's Limitations in Federated Fine-Tuning of Large Language Models
por: Mahla, Navyansh, et al.
Publicado: (2024)

AirLLM: Diffusion Policy-based Adaptive LoRA for Remote Fine-Tuning of LLM over the Air
por: Yang, Shiyi, et al.
Publicado: (2025)

LoRA on the Go: Instance-level Dynamic LoRA Selection and Merging
por: Lee, Seungeon, et al.
Publicado: (2025)

LoRA-One: One-Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently
por: Zhang, Yuanhe, et al.
Publicado: (2025)

Robust Federated Finetuning of LLMs via Alternating Optimization of LoRA
por: Chen, Shuangyi, et al.
Publicado: (2025)