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

Optimizing Large Language Models for Robust Domain-Specific Text-to-SQL: From Prompting to Preference Alignment

Bridging the Language Gap in Text-to-SQL: Adapting LLMs for Chichewa in a Low-Resource Setting

Recent advances in Large Language Models (LLMs) have significantly improved Text-to-SQL performance in high-resource languages. However, their effectiveness in low-resource language settings remains largely underexplored. In this work, we investigate the adaptation of LLMs for Text-to-SQL generation in Chichewa, a low-resource Bantu language spoken by over 12 million people in Malawi and neighboring regions. We construct a structured Chichewa Text-to-SQL benchmark consisting of 400 manually curated natural language–SQL pairs grounded in a unified relational database covering agriculture, commodity prices, population statistics, market data, and food insecurity. We systematically evaluate five open-source LLMs under zero-shot, random 5-shot, and retrieval-augmented 5-shot prompting, in both English and Chichewa. We then apply parameter-efficient fine-tuning (QLoRA) to selected models and, crucially, evaluate the combined effect of QLoRA fine-tuning with retrieval-augmented prompting. QLoRA alone improves English execution accuracy to 78.3% and Chichewa execution accuracy to 41.7%. When combined with retrieval-augmented prompting, QLoRA achieves 53.3% execution accuracy in Chichewa, representing the best reported result for this language on this benchmark and narrowing the English–Chichewa gap to 23.4 percentage points. Our findings offer practical guidance for deploying database interfaces in linguistically underserved environments.

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 reliability of these generated queries at inference time is a central challenge. Conventional methods rely on coarse execution-based signals, which may limit their ability to capture the nuanced semantic alignment required for high-stakes database environments. In this work, we propose the use of Outcome Reward Models (ORMs) as a fine-grained, probabilistic feedback mechanism for test-time verification in Text-to-SQL tasks. We introduce GradeSQL, a framework for training task-specific ORMs that assign scalar utility scores to candidate SQL queries based on their semantic correctness and alignment with database schema. Our approach is evaluated on the BIRD and Spider benchmarks across multiple open-source LLM families. Experimental results demonstrate that ORM-based verification consistently outperforms traditional execution-based heuristics.

Schema-Guided Prompt Strategies for Text-to-SQL over Relational Databases Using Local LLMs

It is suggested that structured prompt engineering provides a practical alternative to model fine-tuning for locally deployed LLMs, offering an effective balance between SQL generation accuracy, computational efficiency, and data privacy.

SPOC-SQL: Stage-wise Preference Optimization for Controllable Text-to-SQL

SPOC-SQL is proposed, which decomposes Text-to-SQL into four sequential subtasks following standard SQL execution logic and designs stage-specific optimization strategies for the model to learn key decisions, with the objective of enhancing structured decision-making during query construction.

Evaluation of Large Language Models for Natural Language to SQL Query Generation: A Comparative Study Using Exact Match and Execution Accuracy

Decisions are being made based on data, and the demands for easy-to-use interfaces that enable users to simply type in a question to a relational database without having to be a Structured Query Language (SQL) expert have increased. Although the development of Large Language Models (LLMs) has seen remarkable advancements in the past few years, and has led to the emergence of LLMs that have significantly improved the generation of SQL from natural language (Text-to-SQL)[1], there is a relative lack of systematic comparisons between the latest commercial LLMs that has been tested against a series of graded SQL complexity levels with both syntactic and execution-based metrics. This research aims to assess and contrast ChatGPT, Google Gemini, DeepSeek, and Microsoft Copilot for their accuracy and efficacy in creating SQL queries from natural language queries. In a quantitative experimental design, 100 natural language questions were given using a purpose-built benchmark, with six types of queries that included simple retrieval, filtering, joins, aggregation, GROUP BY and HAVING, and nested subqueries. The results of the models were assessed using the manually written and validated ground truth query, with the use of normalised Exact Match Accuracy (EMA) and Execution Accuracy (EXA). The best overall results (EMA 97%, EXA 97%) were obtained by ChatGPT. The Copilot scored EMA 56% and EXA 96%, DeepSeek EMA 55% and EXA 93% and Gemini the lowest with a score of EMA 36% and EXA 87%. The large margin between EMA and EXA for the models (+51 points, +40 points, +38 points) demonstrates the significant shortcomings of exact-match scoring when it comes to assessing real-world skills for producing valid but structurally different SQL. The study empirically demonstrates comparative behaviours of the current LLMs, and suggests the use of execution-aware evaluation in the studies and deployment of Text-to-SQL.