12 papers
Augur: Modeling Covariate Causal Associations in Time Series via Large Language Models
Zhiqing Cui, Binwu Wang, Qingxiang Liu +4
Large language models (LLM) have emerged as a promising avenue for time series forecasting, offering the potential to integrate multimodal data. However, existing LLM-based approac…
AgentSafe: Safeguarding Large Language Model-based Multi-agent Systems via Hierarchical Data Management
Junyuan Mao, Fanci Meng, Yifan Duan +6
Large Language Model based multi-agent systems are revolutionizing autonomous communication and collaboration, yet they remain vulnerable to security threats like unauthorized acce…
UrbanVLP: Multi-Granularity Vision-Language Pretraining for Urban Socioeconomic Indicator Prediction
Xixuan Hao, Wei Chen, Yibo Yan +4
Urban socioeconomic indicator prediction aims to infer various metrics related to sustainable development in diverse urban landscapes using data-driven methods. However, prevalent…
DynST: Dynamic Sparse Training for Resource-Constrained Spatio-Temporal Forecasting
Hao Wu, Haomin Wen, Guibin Zhang +5
The ever-increasing sensor service, though opening a precious path and providing a deluge of earth system data for deep-learning-oriented earth science, sadly introduce a daunting…
Mind Scramble: Unveiling Large Language Model Psychology Via Typoglycemia
Miao Yu, Junyuan Mao, Guibin Zhang +7
Research into the external behaviors and internal mechanisms of large language models (LLMs) has shown promise in addressing complex tasks in the physical world. Studies suggest th…
GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning
Guibin Zhang, Haonan Dong, Yuchen Zhang +7
Training high-quality deep models necessitates vast amounts of data, resulting in overwhelming computational and memory demands. Recently, data pruning, distillation, and coreset s…