7 citations · 14 across the 22 of their papers we have counts for
6 papers · 1 filter
Self-Evolving Multi-Agent Collaboration Networks for Software Development
Yue Hu, Yuzhu Cai, Yaxin Du +6
LLM-driven multi-agent collaboration (MAC) systems have demonstrated impressive capabilities in automatic software development at the function level. However, their heavy reliance…
Data Quality Control in Federated Instruction-tuning of Large Language Models
Yaxin Du, Rui Ye, Fengting Yuchi +4
Federated Learning (FL) enables privacy-preserving collaborative instruction tuning of large language models (LLMs) by leveraging massively distributed data. However, the decentral…
Optimizing Cross-Client Domain Coverage for Federated Instruction Tuning of Large Language Models
Zezhou Wang, Yaxin Du, Xingjun Ma +3
Federated domain-specific instruction tuning (FedDIT) for large language models (LLMs) aims to enhance performance in specialized domains using distributed private and limited data…
FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models
Rui Ye, Rui Ge, Xinyu Zhu +5
Federated learning has enabled multiple parties to collaboratively train large language models without directly sharing their data (FedLLM). Following this training paradigm, the c…
Enhancing Data Quality in Federated Fine-Tuning of Foundation Models
Wanru Zhao, Yaxin Du, Nicholas Donald Lane +2
In the current landscape of foundation model training, there is a significant reliance on public domain data, which is nearing exhaustion according to recent research. To further s…
OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning
Rui Ye, Wenhao Wang, Jingyi Chai +6
Trained on massive publicly available data, large language models (LLMs) have demonstrated tremendous success across various fields. While more data contributes to better performan…