Home > Research > Publications & Outputs > Enhancing Text-to-SQL Translation for Financial...

Electronic data

  • 2312.14725v1

    Accepted author manuscript, 917 KB, PDF document

    Available under license: CC BY: Creative Commons Attribution 4.0 International License


View graph of relations

Enhancing Text-to-SQL Translation for Financial System Design

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

  • Yewei Song
  • Saad Ezzini
  • Xunzhu Tang
  • Cedric Lothritz
  • Jacques Klein
  • Tegawendé Bissyandé
  • Andrey Boytsov
  • Ulrick Ble
  • Anne Goujon
Publication date22/12/2023
Host publicationICSE-SEIP 2024
<mark>Original language</mark>English


Text-to-SQL, the task of translating natural language questions into SQL queries, is part of various business processes. Its automation, which is an emerging challenge, will empower software practitioners to seamlessly interact with relational databases using natural language, thereby bridging the gap between business needs and software capabilities. In this paper, we consider Large Language Models (LLMs), which have achieved state of the art for various NLP tasks. Specifically, we benchmark Text-to-SQL performance, the evaluation methodologies, as well as input optimization (e.g., prompting). In light of the empirical observations that we have made, we propose two novel metrics that were designed to adequately measure the similarity between SQL queries. Overall, we share with the community various findings, notably on how to select the right LLM on Text-to-SQL tasks. We further demonstrate that a tree-based edit distance constitutes a reliable metric for assessing the similarity between generated SQL queries and the oracle for benchmarking Text2SQL approaches. This metric is important as it relieves researchers from the need to perform computationally expensive experiments such as executing generated queries as done in prior works. Our work implements financial domain use cases and, therefore contributes to the advancement of Text2SQL systems and their practical adoption in this domain.