研究人员开发了新的方法来提高文本到SQL系统的准确性，该系统将自然语言问题转换为数据库查询。TAHOE使用自动提示优化系统从错误中学习并指导大型语言模型，显著提高了在Spider 2.0-Snow等基准测试上的性能。SOMA-SQL通过生成合成查询日志并使用执行探测来解决含糊不清或不明确的问题，其表现优于最先进的基线。ZAS-SQL从失败案例中提炼规则以提高零样本文本到SQL的性能，确立了新的最先进水平，超越了一些少样本和微调方法。 AI
影响 文本到SQL系统的这些进步可以显著提高更广泛用户访问和查询数据库的效率。
排序理由 多篇研究论文介绍了文本到SQL系统的新方法。
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arXiv:2606.12387v1 Announce Type: cross Abstract: Large Language Models (LLMs) have democratized database access through Text-to-SQL, but moving from prototypes to production remains difficult. Real deployments must handle strict SQL dialects, massive schemas, and evolving user p…
arXiv:2606.11424v1 Announce Type: new Abstract: Natural language interfaces to databases aim to translate user questions into executable SQL, yet remain brittle in real-world settings where questions are underspecified and schemas are large and ambiguous. Ambiguity across user qu…
Large Language Models (LLMs) have democratized database access through Text-to-SQL, but moving from prototypes to production remains difficult. Real deployments must handle strict SQL dialects, massive schemas, and evolving user preferences, while supervised fine-tuning is costly…
Text-to-SQL translates natural language into executable SQL queries. Few-shot in-context learning methods built upon large language models (LLMs) achieve strong performance, yet their reliance on demonstrations limits cross-domain generalization and consumes substantial context w…