4 papers
GTA-RAG: Graph-Trajectory-Augmented Reinforcement Learning for Multi-Turn Retrieval-Augmented Reasoning
Jun Chen, Yongchao Liu, Pengyu Qiu +6
Retrieval-augmented generation (RAG) enables LLMs to access external knowledge for answering knowledge-intensive questions. For complex multi-hop questions, multi-turn retrieval-au…
Robustness in Text-Attributed Graph Learning: Insights, Trade-offs, and New Defenses
Runlin Lei, Lu Yi, Mingguo He +4
While Graph Neural Networks (GNNs) and Large Language Models (LLMs) are powerful approaches for learning on Text-Attributed Graphs (TAGs), a comprehensive understanding of their ro…
HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning
Pengyu Qiu, Xuhong Zhang, Shouling Ji +3
Vertical Federated Learning (VFL) is a trending collaborative machine learning model training solution. Existing industrial frameworks employ secure multi-party computation techniq…
Hijack Vertical Federated Learning Models As One Party
Pengyu Qiu, Xuhong Zhang, Shouling Ji +4
Vertical federated learning (VFL) is an emerging paradigm that enables collaborators to build machine learning models together in a distributed fashion. In general, these parties h…