most citedHarnessing Scalable Transactional Stream Processing for Managing Large Language Models [Vision]

4 citations · 5 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL20231 cited

Online Continual Knowledge Learning for Language Models

Yuhao Wu, Tongjun Shi, Karthick Sharma +2

Large Language Models (LLMs) serve as repositories of extensive world knowledge, enabling them to perform tasks such as question-answering and fact-checking. However, this knowledg…

cs.DB2023

MorphStream: Scalable Processing of Transactions over Streams on Multicores

Yancan Mao, Jianjun Zhao, Zhonghao Yang +3

Transactional Stream Processing Engines (TSPEs) form the backbone of modern stream applications handling shared mutable states. Yet, the full potential of these systems, specifical…

cs.NI2023

TransNFV: Integrating Transactional Semantics for Efficient State Management in Virtual Network Functions

Zhonghao Yang, Shuhao Zhang, Binbin Chen

Managing shared mutable states in high concurrency state access operations is a persistent challenge in Network Functions Virtualization (NFV). This is particularly true when striv…

cs.DB20234 cited

Harnessing Scalable Transactional Stream Processing for Managing Large Language Models [Vision]

Shuhao Zhang, Xianzhi Zeng, Yuhao Wu +1

Large Language Models (LLMs) have demonstrated extraordinary performance across a broad array of applications, from traditional language processing tasks to interpreting structured…

cs.AI2023

SVDE: Scalable Value-Decomposition Exploration for Cooperative Multi-Agent Reinforcement Learning

Shuhan Qi, Shuhao Zhang, Qiang Wang +3

Value-decomposition methods, which reduce the difficulty of a multi-agent system by decomposing the joint state-action space into local observation-action spaces, have become popul…