PLForge: Enhancing Language Models for Natural Language to Procedural Extensions of SQL

Procedural Language extensions of SQL (abbr. PL/SQL) enhance database programming by integrating procedural constructs with SQL's declarative syntax, thereby improving the reusability, modularity, and maintainability of SQL. Besides, PL/SQL in database systems presents significant challenges in real-world development, primarily due to the inherent complexity of programming. To reduce the development difficulty of PL/SQL, this paper studies the novel task of translating natural language (NL) to PL/SQL (i.e., NL-to-PL/SQL), aimed at simplifying PL/SQL development. Recent advancements in language models have shown promise in translating natural language questions into SQL queries (i.e., Text-to-SQL). However, the state-of-the-art Text-to-SQL methods focus only on single SQL queries, neglecting the procedural extensions of SQL, which limits their effectiveness for the NL-to-PL/SQL task. In this paper, we propose PLForge, a suite of pre-trained language models with parameter configurations of 3B, 7B, and 15B, tailored for NL-to-PL/SQL tasks. To enhance the PL/SQL generation capabilities of PLForge, we leverage a curated PL/SQL-centric data corpus and employ an incremental pre-training approach. Furthermore, to fully exploit the potential of PLForge, we propose a comprehensive prompt construction strategy tailored specifically for PL/SQL. Given the scarcity of NL-to-PL/SQL datasets, we develop a template-based method for generating NL-to-PL/SQL data. We conduct a series of experiments on PLForge and several baseline models. Based on execution match and exact match metrics that are designed specifically for the NL-to-PL/SQL task, the experimental results demonstrate that PLForge outperforms existing models in both in-context learning and supervised fine-tuning settings.