Rationalization Models for Text-to-SQL

• URL: http://arxiv.org/abs/2502.06759v2
• Date: Thu, 13 Feb 2025 17:12:34 GMT
• Title: Rationalization Models for Text-to-SQL
• Authors: Gaetano Rossiello, Nhan Pham, Michael Glass, Junkyu Lee, Dharmashankar Subramanian,
• Abstract summary: We introduce a framework for generating Chain-of-Thought (CoT) rationales to enhance text-to-thought model fine-tuning. The process begins with manually annotating a small set of examples, which are then used to prompt a large language model. A rationalization model is subsequently trained on the validated queries, enabling extensive synthetic CoT annotations.
• Score: 13.792561265515003
• License:
• Abstract: We introduce a framework for generating Chain-of-Thought (CoT) rationales to enhance text-to-SQL model fine-tuning. These rationales consist of intermediate SQL statements and explanations, serving as incremental steps toward constructing the final SQL query. The process begins with manually annotating a small set of examples, which are then used to prompt a large language model in an iterative, dynamic few-shot knowledge distillation procedure from a teacher model. A rationalization model is subsequently trained on the validated decomposed queries, enabling extensive synthetic CoT annotations for text-to-SQL datasets. To evaluate the approach, we fine-tune small language models with and without these rationales on the BIRD dataset. Results indicate that step-by-step query generation improves execution accuracy, especially for moderately and highly complex queries, while also enhancing explainability.

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