9 citations · 22 across the 16 of their papers we have counts for
6 papers · 1 filter
Are We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review
Rui Ye, Xianghe Pang, Jingyi Chai +6
Scholarly peer review is a cornerstone of scientific advancement, but the system is under strain due to increasing manuscript submissions and the labor-intensive nature of the proc…
KnowledgeSG: Privacy-Preserving Synthetic Text Generation with Knowledge Distillation from Server
Wenhao Wang, Xiaoyu Liang, Rui Ye +3
The success of large language models (LLMs) facilitate many parties to fine-tune LLMs on their own private data. However, this practice raises privacy concerns due to the memorizat…
Leveraging Unstructured Text Data for Federated Instruction Tuning of Large Language Models
Rui Ye, Rui Ge, Yuchi Fengting +3
Federated instruction tuning enables multiple clients to collaboratively fine-tune a shared large language model (LLM) that can follow humans' instructions without directly sharing…
Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models
Rui Ye, Jingyi Chai, Xiangrui Liu +3
Federated learning (FL) enables multiple parties to collaboratively fine-tune an large language model (LLM) without the need of direct data sharing. Ideally, by training on decentr…
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…
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…