Imagen de Portada

Building a Family of Data Augmentation Models for Low-cost LLM Fine-tuning on the Cloud

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Yue, Yuanhao, Wang, Chengyu, Huang, Jun, Wang, Peng
Formato:	Preprint
Publicado:	2024
Materias:	Computation and Language
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Distilling Instruction-following Abilities of Large Language Models with Task-aware Curriculum Planning
por: Yue, Yuanhao, et al.
Publicado: (2024)

DistilQwen2.5: Industrial Practices of Training Distilled Open Lightweight Language Models
por: Wang, Chengyu, et al.
Publicado: (2025)

OmniThoughtVis: A Scalable Distillation Pipeline for Deployable Multimodal Reasoning Models
por: Yue, Yuanhao, et al.
Publicado: (2026)

AgenticQwen: Training Small Agentic Language Models with Dual Data Flywheels for Industrial-Scale Tool Use
por: Lyu, Yuanjie, et al.
Publicado: (2026)

Data-efficient LLM Fine-tuning for Code Generation
por: Lv, Weijie, et al.
Publicado: (2025)

EasyDistill: A Comprehensive Toolkit for Effective Knowledge Distillation of Large Language Models
por: Wang, Chengyu, et al.
Publicado: (2025)

LLM-AutoDP: Automatic Data Processing via LLM Agents for Model Fine-tuning
por: Huang, Wei, et al.
Publicado: (2026)

Memento: Fine-tuning LLM Agents without Fine-tuning LLMs
por: Zhou, Huichi, et al.
Publicado: (2025)

Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC
por: Wang, Qingzheng, et al.
Publicado: (2025)

Be Careful When Fine-tuning On Open-Source LLMs: Your Fine-tuning Data Could Be Secretly Stolen!
por: Zhang, Zhexin, et al.
Publicado: (2025)

Mock Worlds, Real Skills: Building Small Agentic Language Models with Synthetic Tasks, Simulated Environments, and Rubric-Based Rewards
por: Lyu, Yuanjie, et al.
Publicado: (2026)

Token-level Data Selection for Safe LLM Fine-tuning
por: Li, Yanping, et al.
Publicado: (2026)

Training Language Models to Generate Text with Citations via Fine-grained Rewards
por: Huang, Chengyu, et al.
Publicado: (2024)

Investigating the Representation of Backchannels and Fillers in Fine-tuned Language Models
por: Wang, Yu, et al.
Publicado: (2025)

PMSS: Pretrained Matrices Skeleton Selection for LLM Fine-tuning
por: Wang, Qibin, et al.
Publicado: (2024)

EoRA: Fine-tuning-free Compensation for Compressed LLM with Eigenspace Low-Rank Approximation
por: Liu, Shih-Yang, et al.
Publicado: (2024)

Agent Data Protocol: Unifying Datasets for Diverse, Effective Fine-tuning of LLM Agents
por: Song, Yueqi, et al.
Publicado: (2025)

A General Framework to Enhance Fine-tuning-based LLM Unlearning
por: Ren, Jie, et al.
Publicado: (2025)

R4: Reinforced Retriever-Reorder-Responder for Retrieval-Augmented Large Language Models
por: Zhang, Taolin, et al.
Publicado: (2024)

LoX: Low-Rank Extrapolation Robustifies LLM Safety Against Fine-tuning
por: Perin, Gabriel J., et al.
Publicado: (2025)

An Empirical Study of LLM-as-a-Judge for LLM Evaluation: Fine-tuned Judge Model is not a General Substitute for GPT-4
por: Huang, Hui, et al.
Publicado: (2024)

Fine-tuning Done Right in Model Editing
por: Yang, Wanli, et al.
Publicado: (2025)

LoFiT: Localized Fine-tuning on LLM Representations
por: Yin, Fangcong, et al.
Publicado: (2024)

Impact of Model Size on Fine-tuned LLM Performance in Data-to-Text Generation: A State-of-the-Art Investigation
por: Mahapatra, Joy, et al.
Publicado: (2024)

PocketLLM: Enabling On-Device Fine-Tuning for Personalized LLMs
por: Peng, Dan, et al.
Publicado: (2024)

Aloe: A Family of Fine-tuned Open Healthcare LLMs
por: Gururajan, Ashwin Kumar, et al.
Publicado: (2024)

As Simple as Fine-tuning: LLM Alignment via Bidirectional Negative Feedback Loss
por: Mao, Xin, et al.
Publicado: (2024)

Large Language Model for Multi-Domain Translation: Benchmarking and Domain CoT Fine-tuning
por: Hu, Tianxiang, et al.
Publicado: (2024)

AlignSum: Data Pyramid Hierarchical Fine-tuning for Aligning with Human Summarization Preference
por: Han, Yang, et al.
Publicado: (2024)

DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression
por: Huang, Wei, et al.
Publicado: (2025)

Panacea: Mitigating Harmful Fine-tuning for Large Language Models via Post-fine-tuning Perturbation
por: Wang, Yibo, et al.
Publicado: (2025)

Exploring Memorization in Fine-tuned Language Models
por: Zeng, Shenglai, et al.
Publicado: (2023)

On the Loss of Context-awareness in General Instruction Fine-tuning
por: Wang, Yihan, et al.
Publicado: (2024)

Building Dialogue Understanding Models for Low-resource Language Indonesian from Scratch
por: Di, Donglin, et al.
Publicado: (2024)

Improving Data Efficiency for LLM Reinforcement Fine-tuning Through Difficulty-targeted Online Data Selection and Rollout Replay
por: Sun, Yifan, et al.
Publicado: (2025)

ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models
por: Liu, Zequan, et al.
Publicado: (2024)

JudgeLM: Fine-tuned Large Language Models are Scalable Judges
por: Zhu, Lianghui, et al.
Publicado: (2023)

Two-stage LLM Fine-tuning with Less Specialization and More Generalization
por: Wang, Yihan, et al.
Publicado: (2022)

Do Large Language Models Understand Logic or Just Mimick Context?
por: Yan, Junbing, et al.
Publicado: (2024)

From Correction to Mastery: Reinforced Distillation of Large Language Model Agents
por: Lyu, Yuanjie, et al.
Publicado: (2025)