24 citations · 38 across the 4 of their papers we have counts for
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cs.HC2026
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,…
cs.HC2025
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…
cs.HC2022★ 24 cited
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,…