# Enhancing Text-to-SQL Translation for Financial System Design

arXiv: https://arxiv.org/abs/2312.14725 · HTML: https://mlsanity.com/paper/2312.14725/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2023.

## Built on by (most cited first, 12 of 12)

- [Revisiting Code Similarity Evaluation with Abstract Syntax Tree Edit Distance](https://mlsanity.com/paper/2404.08817/) (2024)
- [EvoSchema: Towards Text-to-SQL Robustness Against Schema Evolution](https://mlsanity.com/paper/2603.10697/) (2026)
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- [Structuring the Unstructured: A Multi-Agent System for Extracting and Querying Financial KPIs and Guidance](https://mlsanity.com/paper/2505.19197/) (2025)
- [E3-Rewrite: Learning to Rewrite SQL for Executability, Equivalence,and Efficiency](https://mlsanity.com/paper/2508.09023/) (2025)
- [NL2SQLBench: A Modular Benchmarking Framework for LLM-Enabled NL2SQL Solutions](https://mlsanity.com/paper/2604.16493/) (2026)
- [Macro-Queries: An Exploration into Guided Chart Generation from High Level Prompts](https://mlsanity.com/paper/2408.12726/) (2024)
- [LeGo-Code: Can Modular Curriculum Learning Advance Complex Code Generation? Insights from Text-to-SQL](https://mlsanity.com/paper/2604.18254/) (2026)
- [Policy-Conditioned Constrained Decoding for Column-Level Access Control in Text-to-SQL](https://mlsanity.com/paper/2607.12341/) (2026)
- [StocksTalk: A Voice-Enabled Conversational Agent for Structured Query Generation over Web Data](https://mlsanity.com/paper/2608.18105/) (2026)

## Abstract (arXiv)

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.