3 papers
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.HC2025
Xavier: Toward Better Coding Assistance in Authoring Tabular Data Wrangling Scripts
Yunfan Zhou, Xiwen Cai, Qiming Shi +5
Data analysts frequently employ code completion tools in writing custom scripts to tackle complex tabular data wrangling tasks. However, existing tools do not sufficiently link the…