Understanding, Detecting, and Repairing Real-World In-Context-Learning-Based Text-to-SQL Errors
arXiv:2501.09310v3 Announce Type: replace Abstract: Large language models (LLMs) have been adopted for text-to-SQL tasks, utilizing their in-context learning (ICL) capability to translate natural language questions into SQL queries. However, such a technique faces correctness problems. In this paper, we conduct the first comprehensive study of text-to-SQL errors of ICL-based techniques. Our study covers four representative ICL-based techniques, five basic repairing methods, two benchmarks, and two LLM settings. We find that text-to-SQL errors are widespread and summarize 27 error types of 7 ca
The increasing adoption of large language models for practical applications like text-to-SQL is naturally surfacing foundational challenges related to correctness and reliability.
Understanding and mitigating errors in LLM-based text-to-SQL systems is crucial for their reliable deployment in enterprise environments, impacting automation and data access.
This research provides a structured approach to identifying and addressing practical limitations in a key application of LLMs, enabling more robust and trustworthy AI systems.
- · AI developers
- · Enterprises adopting LLM solutions
- · Data scientists
- · LLM providers with unaddressed error modes
- · Businesses relying on unreliable text-to-SQL automation
Improved reliability and trust in LLM-driven data interaction tools will lead to their wider adoption.
Enhanced debugging and error-correction capabilities will become a standard feature in AI development toolchains, accelerating LLM deployment.
The systematization of error types could lead to the development of 'self-healing' or 'error-aware' LLM architectures that anticipate and correct their own mistakes.
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