Any text-to-SQL benchmark should address difficulties of real-world data stores
cacm.acm.org
July 22, 2026
1 min read
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45/100
Summary
Text-to-SQL benchmarks must consider the complexities and challenges posed by real-world data stores. Effective evaluation should include factors like data variability, schema complexity, and query performance.
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Community Sentiment
Positives
- SQL generation is quite good, 90% of the problem is data quality — if you clean up your data, LLMs can shine in generating queries.
- Text to SQL can absolutely work if you design the system correctly — with the right architecture, it’s not just a pipe dream.
- Involving a human operator in the query generation loop can significantly improve results — it’s all about collaboration between LLMs and users.
Concerns
- Business users treating LLM-generated SQL as gospel is a recipe for disaster — without validation, critical errors could go unchecked.
- An LLM can't read the user's mind to eliminate ambiguity — until that changes, misinterpretations will persist.
- Single-shot approaches to SQL generation are a dead end — without iterative hypothesis generation, you're just guessing.
