NameGuess: Column Name Expansion for Tabular Data
Jiani Zhang Zhengyuan Shen Balasubramaniam Srinivasan Shen Wang Huzefa Rangwala George Karypis
Recent advances in large language models have revolutionized many sectors,
including the database industry. One common challenge when dealing with large
volumes of tabular data is the pervasive use of abbreviated column names, which
can negatively impact performance on various data search, access, and
understanding tasks. To address this issue, we introduce a new task, called
NameGuess, to expand column names (used in database schema) as a natural
language generation problem. We create a training dataset of 384K
abbreviated-expanded column pairs using a new data fabrication method and a
human-annotated evaluation benchmark that includes 9.2K examples from
real-world tables. To tackle the complexities associated with polysemy and
ambiguity in NameGuess, we enhance auto-regressive language models by
conditioning on table content and column header names -- yielding a fine-tuned
model (with 2.7B parameters) that matches human performance. Furthermore, we
conduct a comprehensive analysis (on multiple LLMs) to validate the
effectiveness of table content in NameGuess and identify promising future
opportunities. Code has been made available at
https://github.com/amazon-science/nameguess.