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cs.CR2024
On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains
Xun Xian, Ganghua Wang, Xuan Bi +5
Retrieval-Augmented Generation (RAG) has been empirically shown to enhance the performance of large language models (LLMs) in knowledge-intensive domains such as healthcare, financ…
cs.LG2024★ 2 cited
MoveLight: Enhancing Traffic Signal Control through Movement-Centric Deep Reinforcement Learning
Junqi Shao, Chenhao Zheng, Yuxuan Chen +2
This paper introduces MoveLight, a novel traffic signal control system that enhances urban traffic management through movement-centric deep reinforcement learning. By leveraging de…