Saved in:
| Main Authors: | Du, Yaxin, Ye, Rui, Yuchi, Fengting, Zhao, Wanru, Qu, Jingjing, Wang, Yanfeng, Chen, Siheng |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.11540 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Enhancing Data Quality in Federated Fine-Tuning of Foundation Models
by: Zhao, Wanru, et al.
Published: (2024)
by: Zhao, Wanru, et al.
Published: (2024)
Leveraging Unstructured Text Data for Federated Instruction Tuning of Large Language Models
by: Ye, Rui, et al.
Published: (2024)
by: Ye, Rui, et al.
Published: (2024)
FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models
by: Ye, Rui, et al.
Published: (2024)
by: Ye, Rui, et al.
Published: (2024)
OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning
by: Ye, Rui, et al.
Published: (2024)
by: Ye, Rui, et al.
Published: (2024)
Optimizing Cross-Client Domain Coverage for Federated Instruction Tuning of Large Language Models
by: Wang, Zezhou, et al.
Published: (2024)
by: Wang, Zezhou, et al.
Published: (2024)
FedMABench: Benchmarking Mobile Agents on Decentralized Heterogeneous User Data
by: Wang, Wenhao, et al.
Published: (2025)
by: Wang, Wenhao, et al.
Published: (2025)
Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models
by: Ye, Rui, et al.
Published: (2024)
by: Ye, Rui, et al.
Published: (2024)
VLMGuard-R1: Proactive Safety Alignment for VLMs via Reasoning-Driven Prompt Optimization
by: Chen, Menglan, et al.
Published: (2025)
by: Chen, Menglan, et al.
Published: (2025)
Learn What You Need in Personalized Federated Learning
by: Lv, Kexin, et al.
Published: (2024)
by: Lv, Kexin, et al.
Published: (2024)
Decentralized and Lifelong-Adaptive Multi-Agent Collaborative Learning
by: Tang, Shuo, et al.
Published: (2024)
by: Tang, Shuo, et al.
Published: (2024)
Hypergraph Transformer for Semi-Supervised Classification
by: Liu, Zexi, et al.
Published: (2023)
by: Liu, Zexi, et al.
Published: (2023)
Federated Data-Efficient Instruction Tuning for Large Language Models
by: Qin, Zhen, et al.
Published: (2024)
by: Qin, Zhen, et al.
Published: (2024)
The Future of Large Language Model Pre-training is Federated
by: Sani, Lorenzo, et al.
Published: (2024)
by: Sani, Lorenzo, et al.
Published: (2024)
A Survey on Federated Fine-tuning of Large Language Models
by: Wu, Yebo, et al.
Published: (2025)
by: Wu, Yebo, et al.
Published: (2025)
Sparse is Enough in Fine-tuning Pre-trained Large Language Models
by: Song, Weixi, et al.
Published: (2023)
by: Song, Weixi, et al.
Published: (2023)
AceGRPO: Adaptive Curriculum Enhanced Group Relative Policy Optimization for Autonomous Machine Learning Engineering
by: Cai, Yuzhu, et al.
Published: (2026)
by: Cai, Yuzhu, et al.
Published: (2026)
Exploring Memorization in Fine-tuned Language Models
by: Zeng, Shenglai, et al.
Published: (2023)
by: Zeng, Shenglai, et al.
Published: (2023)
Exploring Federated Pruning for Large Language Models
by: Guo, Pengxin, et al.
Published: (2025)
by: Guo, Pengxin, et al.
Published: (2025)
Learning Instruction-Following Policies through Open-Ended Instruction Relabeling with Large Language Models
by: Zhang, Zhicheng, et al.
Published: (2025)
by: Zhang, Zhicheng, et al.
Published: (2025)
Are We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review
by: Ye, Rui, et al.
Published: (2024)
by: Ye, Rui, et al.
Published: (2024)
InstructAV: Instruction Fine-tuning Large Language Models for Authorship Verification
by: Hu, Yujia, et al.
Published: (2024)
by: Hu, Yujia, et al.
Published: (2024)
Adapting Large Language Models for Content Moderation: Pitfalls in Data Engineering and Supervised Fine-tuning
by: Ma, Huan, et al.
Published: (2023)
by: Ma, Huan, et al.
Published: (2023)
Fine-tuning Large Language Model for Automated Algorithm Design
by: Liu, Fei, et al.
Published: (2025)
by: Liu, Fei, et al.
Published: (2025)
LLMSurgeon: Diagnosing Data Mixture of Large Language Models
by: Luo, Yaxin, et al.
Published: (2026)
by: Luo, Yaxin, et al.
Published: (2026)
Instruction Mining: Instruction Data Selection for Tuning Large Language Models
by: Cao, Yihan, et al.
Published: (2023)
by: Cao, Yihan, et al.
Published: (2023)
Forgetting: A New Mechanism Towards Better Large Language Model Fine-tuning
by: Taheri, Ali, et al.
Published: (2025)
by: Taheri, Ali, et al.
Published: (2025)
Efficient Split Federated Learning for Large Language Models over Communication Networks
by: Zhao, Kai, et al.
Published: (2025)
by: Zhao, Kai, et al.
Published: (2025)
Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization
by: Liu, Yixin, et al.
Published: (2023)
by: Liu, Yixin, et al.
Published: (2023)
Instruction-tuned Language Models are Better Knowledge Learners
by: Jiang, Zhengbao, et al.
Published: (2024)
by: Jiang, Zhengbao, et al.
Published: (2024)
FIT to Forget: Robust Continual Unlearning for Large Language Models
by: Xu, Xiaoyu, et al.
Published: (2026)
by: Xu, Xiaoyu, et al.
Published: (2026)
Federated Knowledge Transfer Fine-tuning Large Server Model with Resource-Constrained IoT Clients
by: Chen, Shaoyuan, et al.
Published: (2024)
by: Chen, Shaoyuan, et al.
Published: (2024)
Instruction Lens Score: Your Instruction Contributes a Powerful Object Hallucination Detector for Multimodal Large Language Models
by: Lai, Runhe, et al.
Published: (2026)
by: Lai, Runhe, et al.
Published: (2026)
TuneComp: Joint Fine-tuning and Compression for Large Foundation Models
by: Chen, Xiangyu, et al.
Published: (2025)
by: Chen, Xiangyu, et al.
Published: (2025)
DataMaster: Data-Centric Autonomous AI Research
by: Du, Yaxin, et al.
Published: (2026)
by: Du, Yaxin, et al.
Published: (2026)
FedEGG: Federated Learning with Explicit Global Guidance
by: Zhai, Kun, et al.
Published: (2024)
by: Zhai, Kun, et al.
Published: (2024)
Selecting Large Language Model to Fine-tune via Rectified Scaling Law
by: Lin, Haowei, et al.
Published: (2024)
by: Lin, Haowei, et al.
Published: (2024)
Efficient Inference Using Large Language Models with Limited Human Data: Fine-Tuning then Rectification
by: Wang, Lei, et al.
Published: (2025)
by: Wang, Lei, et al.
Published: (2025)
Emotion Knowledge Enhancement for Vision Large Language Models: A Self-Verification Approach for High-Quality Emotion Instruction Data Generation
by: Wang, Feifan, et al.
Published: (2025)
by: Wang, Feifan, et al.
Published: (2025)
Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions
by: Wang, Jingtan, et al.
Published: (2024)
by: Wang, Jingtan, et al.
Published: (2024)
Non-instructional Fine-tuning: Enabling Instruction-Following Capabilities in Pre-trained Language Models without Instruction-Following Data
by: Xie, Juncheng, et al.
Published: (2024)
by: Xie, Juncheng, et al.
Published: (2024)
Similar Items
-
Enhancing Data Quality in Federated Fine-Tuning of Foundation Models
by: Zhao, Wanru, et al.
Published: (2024) -
Leveraging Unstructured Text Data for Federated Instruction Tuning of Large Language Models
by: Ye, Rui, et al.
Published: (2024) -
FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models
by: Ye, Rui, et al.
Published: (2024) -
OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning
by: Ye, Rui, et al.
Published: (2024) -
Optimizing Cross-Client Domain Coverage for Federated Instruction Tuning of Large Language Models
by: Wang, Zezhou, et al.
Published: (2024)