Semantically Aligned Question and Code Generation for Automated Insight Generation
arxiv(2024)
摘要
Automated insight generation is a common tactic for helping knowledge
workers, such as data scientists, to quickly understand the potential value of
new and unfamiliar data. Unfortunately, automated insights produced by
large-language models can generate code that does not correctly correspond (or
align) to the insight. In this paper, we leverage the semantic knowledge of
large language models to generate targeted and insightful questions about data
and the corresponding code to answer those questions. Then through an empirical
study on data from Open-WikiTable, we show that embeddings can be effectively
used for filtering out semantically unaligned pairs of question and code.
Additionally, we found that generating questions and code together yields more
diverse questions.
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