# Fundamental Challenges in Evaluating Text2SQL Solutions and Detecting Their Limitations

arXiv: https://arxiv.org/abs/2501.18197 · HTML: https://mlsanity.com/paper/2501.18197/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2025.

## Built on by (most cited first, 5 of 5)

- [PaVeRL-SQL: Text-to-SQL via Partial-Match Rewards and Verbal Reinforcement Learning](https://mlsanity.com/paper/2509.07159/) (2025)
- [Beyond Query-Level Comparison: Fine-Grained Reinforcement Learning for Text-to-SQL with Automated Interpretable Critiques](https://mlsanity.com/paper/2511.22258/) (2025)
- [GBV-SQL: Guided Generation and SQL2Text Back-Translation Validation for Multi-Agent Text2SQL](https://mlsanity.com/paper/2509.12612/) (2025)
- [EGREFINE: An Execution-Grounded Optimization Framework for Text-to-SQL Schema Refinement](https://mlsanity.com/paper/2605.00628/) (2026)
- [SQLMorph: Query Mutation and Fine-Grained Metrics for Text-to-SQL Evaluation](https://mlsanity.com/paper/2609.08950/) (2026)

## Abstract (arXiv)

In this work, we dive into the fundamental challenges of evaluating Text2SQL solutions and highlight potential failure causes and the potential risks of relying on aggregate metrics in existing benchmarks. We identify two largely unaddressed limitations in current open benchmarks: (1) data quality issues in the evaluation data, mainly attributed to the lack of capturing the probabilistic nature of translating a natural language description into a structured query (e.g., NL ambiguity), and (2) the bias introduced by using different match functions as approximations for SQL equivalence. To put both limitations into context, we propose a unified taxonomy of all Text2SQL limitations that can lead to both prediction and evaluation errors. We then motivate the taxonomy by providing a survey of Text2SQL limitations using state-of-the-art Text2SQL solutions and benchmarks. We describe the causes of limitations with real-world examples and propose potential mitigation solutions for each category in the taxonomy. We conclude by highlighting the open challenges encountered when deploying such mitigation strategies or attempting to automatically apply the taxonomy.