9 citations · 17 across the 5 of their papers we have counts for
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cs.SE2023★ 3 cited
Adaptive Intellect Unleashed: The Feasibility of Knowledge Transfer in Large Language Models
Qing Huang, Yishun Wu, Zhenchang Xing +3
We conduct the first empirical study on using knowledge transfer to improve the generalization ability of large language models (LLMs) in software engineering tasks, which often re…
cs.SE2023★ 4 cited
Prompt Sapper: LLM-Empowered Software Engineering Infrastructure for AI-Native Services
Zhenchang Xing, Qing Huang, Yu Cheng +3
Foundation models, such as GPT-4, DALL-E have brought unprecedented AI "operating system" effect and new forms of human-AI interaction, sparking a wave of innovation in AI-native s…
cs.SE2023★ 1 cited
Prompt Sapper: A LLM-Empowered Production Tool for Building AI Chains
Yu Cheng, Jieshan Chen, Qing Huang +3
The emergence of foundation models, such as large language models (LLMs) GPT-4 and text-to-image models DALL-E, has opened up numerous possibilities across various domains. People…