9 citations · 31 across the 26 of their papers we have counts for
14 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…
Synthesizing Post-Training Data for LLMs through Multi-Agent Simulation
Shuo Tang, Xianghe Pang, Zexi Liu +6
Post-training is essential for enabling large language models (LLMs) to follow human instructions. However, its effectiveness depends on high-quality instruction data, which is cha…
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…
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…
Physical Backdoor Attack can Jeopardize Driving with Vision-Large-Language Models
Zhenyang Ni, Rui Ye, Yuxi Wei +3
Vision-Large-Language-models(VLMs) have great application prospects in autonomous driving. Despite the ability of VLMs to comprehend and make decisions in complex scenarios, their…
Decentralized and Lifelong-Adaptive Multi-Agent Collaborative Learning
Shuo Tang, Rui Ye, Chenxin Xu +3
Decentralized and lifelong-adaptive multi-agent collaborative learning aims to enhance collaboration among multiple agents without a central server, with each agent solving varied…
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…
Self-Alignment of Large Language Models via Monopolylogue-based Social Scene Simulation
Xianghe Pang, Shuo Tang, Rui Ye +4
Aligning large language models (LLMs) with human values is imperative to mitigate potential adverse effects resulting from their misuse. Drawing from the sociological insight that…
Learn What You Need in Personalized Federated Learning
Kexin Lv, Rui Ye, Xiaolin Huang +2
Personalized federated learning aims to address data heterogeneity across local clients in federated learning. However, current methods blindly incorporate either full model parame…
FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units
Shaoheng Fang, Rui Ye, Wenhao Wang +5
Roadside unit (RSU) can significantly improve the safety and robustness of autonomous vehicles through Vehicle-to-Everything (V2X) communication. Currently, the usage of a single R…
ConfusionPrompt: Practical Private Inference for Online Large Language Models
Peihua Mai, Youjia Yang, Ran Yan +2
State-of-the-art large language models (LLMs) are typically deployed as online services, requiring users to transmit detailed prompts to cloud servers. This raises significant priv…