Prompting Strategies for Enterprise Text-to-SQL: An Ablation Study

Iteration Without Elaboration: A Simple ReAct Architecture Suffices for Text-to-SQL Generation

A simple yet effective zero-shot ReAct-style framework built solely on iterative reasoning and a constrained action space defined by a typed Domain-Specific Language (DSL) of 15 relational operations, rather than free-form SQL generation.

Prompt Template-Driven Large Model SQL Generation

GradeSQL: Outcome reward models for intelligent Text-to-SQL generation from LLMs

As Large Language Models (LLMs) become foundational to next-generation Intelligent Information Systems, the bridge between natural language interfaces and structured database systems remains a critical bottleneck. While Text-to-SQL generation enables cooperative support for complex query formulation, ensuring the rel...

SQuaD-SQL: Efficient Text-to-SQL with Small Language Models via LLM-Guided Knowledge Distillation

SQuaD-SQL (Small-Qualified and Distilled for SQL), a novel approach that empowers small language models to approach the performance of LLMs on the Text-to-SQL task while significantly improving efficiency through knowledge distillation and synthetic data generation, is introduced.

Text-to-SQL with Large Language Models: Challenges Revisited and New Dimensions

This position paper argues that each original challenge for LLM-based Text-to-SQL has given rise to a new dimension, and synthesizes these concerns and outlines a research agenda along three horizons, arguing for trustworthiness, interactivity, and economic sustainability as first-class concerns.

ExpeSQL: An Efficient, Experience-Guided Decompositional Search Framework for Text-to-SQL

This work introduces ExpeSQL, a zero-shot, open-source–compatible, and efficient framework that combines divide-and-conquer reasoning, Best-of-N candidate selection, and self-critique with experience-guided refinement that establishes a new paradigm for deployable, self-improving Text-to-SQL systems in dynamic, real-wo...