Technology Category
Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.Knowledge Representation and Reasoning: Computational Complexity of ReasoningData Mining & Knowledge Management: Intelligent Query Processing
Rationalization Models for Text-to-SQL
🤖 AI Summary
To address the low execution accuracy and poor interpretability of medium-to-high-complexity queries in text-to-SQL tasks, this paper proposes a Chain-of-Thought (CoT) rationalization framework tailored for Text-to-SQL. Methodologically, it introduces a novel paradigm combining human-annotated guidance for large language model prompt generation with iterative dynamic few-shot knowledge distillation, enabling efficient construction of high-quality, scalable CoT data annotated with intermediate SQL queries and natural-language explanations. A lightweight rationalization model is then supervisedly trained on this data. Evaluated on the BIRD benchmark, the approach significantly improves execution accuracy—especially for medium-to-high-complexity queries—while generating clear, faithful, step-by-step reasoning traces. Thus, it achieves a favorable trade-off between performance and interpretability, advancing both practical utility and model transparency in Text-to-SQL systems.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Enhance text-to-SQL model fine-tuning
Generate Chain-of-Thought rationales
Improve SQL query execution accuracy
Innovation
Methods, ideas, or system contributions that make the work stand out.
Chain-of-Thought rationales generation
Iterative few-shot knowledge distillation
Enhanced text-to-SQL execution accuracy
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