3 citations · 5 across the 7 of their papers we have counts for
4 papers · 1 filter
DisCEdge: Distributed Context Management for Large Language Models at the Edge
Mohammadreza Malekabbasi, Minghe Wang, David Bermbach
Deploying Large Language Model (LLM) services at the edge benefits latency-sensitive and privacy-aware applications. However, the stateless nature of LLMs makes managing user conte…
Exploring Influence Factors on LLM Suitability for No-Code Development of End User IoT Applications
Minghe Wang, Alexandra Kapp, Trever Schirmer +2
No-Code Development Platforms (NCDPs) empower non-technical end users to build applications tailored to their specific demands without writing code. While NCDPs lower technical bar…
A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment
Kun Wang, Guibin Zhang, Zhenhong Zhou +100
The remarkable success of Large Language Models (LLMs) has illuminated a promising pathway toward achieving Artificial General Intelligence for both academic and industrial communi…
LLM4FaaS: No-Code Application Development using LLMs and FaaS
Minghe Wang, Tobias Pfandzelter, Trever Schirmer +1
Large language models (LLMs) show great capabilities in generating code from natural language descriptions, bringing programming power closer to non-technical users. However, their…