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.
@article{arxiv.2405.01556,
title = {Semantically Aligned Question and Code Generation for Automated Insight Generation},
author = {Ananya Singha and Bhavya Chopra and Anirudh Khatry and Sumit Gulwani and Austin Z. Henley and Vu Le and Chris Parnin and Mukul Singh and Gust Verbruggen},
journal= {arXiv preprint arXiv:2405.01556},
year = {2024}
}