24 citations · 38 across the 4 of their papers we have counts for
4 papers
Cerebra: Aligning Implicit Knowledge in Interactive SQL Authoring
Yunfan Zhou, Qiming Shi, Zhongsu Luo +5
LLM-driven tools have significantly lowered barriers to writing SQL queries. However, user instructions are often underspecified, assuming the model understands implicit knowledge,…
ViseGPT: Towards Better Alignment of LLM-generated Data Wrangling Scripts and User Prompts
Jiajun Zhu, Xinyu Cheng, Zhongsu Luo +4
Large language models (LLMs) enable the rapid generation of data wrangling scripts based on natural language instructions, but these scripts may not fully adhere to user-specified…
Revealing the Semantics of Data Wrangling Scripts With COMANTICS
Kai Xiong, Zhongsu Luo, Siwei Fu +3
Data workers usually seek to understand the semantics of data wrangling scripts in various scenarios, such as code debugging, reusing, and maintaining. However, the understanding i…
Visualizing the Scripts of Data Wrangling with SOMNUS
Kai Xiong, Siwei Fu, Guoming Ding +5
Data workers use various scripting languages for data transformation, such as SAS, R, and Python. However, understanding intricate code pieces requires advanced programming skills,…