A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going? (2408.05109v5)

Abstract: Translating users' natural language queries (NL) into SQL queries (i.e., Text-to-SQL, a.k.a. NL2SQL) can significantly reduce barriers to accessing relational databases and support various commercial applications. The performance of Text-to-SQL has been greatly enhanced with the emergence of LLMs. In this survey, we provide a comprehensive review of Text-to-SQL techniques powered by LLMs, covering its entire lifecycle from the following four aspects: (1) Model: Text-to-SQL translation techniques that tackle not only NL ambiguity and under-specification, but also properly map NL with database schema and instances; (2) Data: From the collection of training data, data synthesis due to training data scarcity, to Text-to-SQL benchmarks; (3) Evaluation: Evaluating Text-to-SQL methods from multiple angles using different metrics and granularities; and (4) Error Analysis: analyzing Text-to-SQL errors to find the root cause and guiding Text-to-SQL models to evolve. Moreover, we offer a rule of thumb for developing Text-to-SQL solutions. Finally, we discuss the research challenges and open problems of Text-to-SQL in the LLMs era.

• The paper systematically categorizes NL2SQL methods into techniques, data, evaluation, and error analysis while highlighting LLM-enabled advancements.
• It presents a detailed review of modular components and benchmark datasets that underscore the strengths and limitations of existing NL2SQL systems.
• The study outlines future research directions, emphasizing adaptive data synthesis and improved error diagnostics to enhance query generation.

A Survey of NL2SQL with LLMs: Where are we, and where are we going?

The purpose of this essay is to provide an expert-level overview of the comprehensive survey titled "A Survey of NL2SQL with LLMs: Where are we, and where are we going?" authored by Xinyu Liu et al. The paper methodically reviews the existing technologies in Natural Language to SQL (NL2SQL) translation and explores the profound impact of LLMs on this domain.

Background and Introduction

NL2SQL serves the critical function of converting natural language queries (NL) into executable SQL statements, thereby reducing the barrier to accessing and leveraging large relational databases. This capability has numerous applications, including business intelligence and customer support, enhancing the usability and accessibility of data science. The paper is timely given the rapid advancements in LLMs, which have significantly augmented the capabilities of NL2SQL systems.

Framework and Lifecycle

The authors present a lifecycle of NL2SQL tasks in Figure 1, encapsulating four main phases: techniques, data, evaluation, and error analysis. This structured approach aids in understanding the intricate challenges faced by NL2SQL systems at various stages of development:

1. Techniques: The paper categorizes NL2SQL techniques into four major areas: NL ambiguity, under-specification handling, NL and database schema mapping, and NL instance processing. LLMs have greatly bolstered these techniques, enabling more effective handling of linguistic nuances and complex query formations.
2. Data: The survey discusses the critical necessity of high-quality training data. It presents statistical analyses of existing benchmarks and methods for data collection and synthesis, underlining training data scarcity as a significant challenge.
3. Evaluation: Multiple dimensions of evaluation are explored, including scenario-based and multi-angle evaluations. This comprehensive evaluation methodology is crucial for optimizing and selecting the best-performing models for different use cases.
4. Error Analysis: Error analysis is emphasized for its role in identifying model limitations and evolving error-handling mechanisms. The paper provides a taxonomy for categorizing NL2SQL errors and principles for designing these taxonomies, illustrating a structured approach to error diagnosis and correction.

Key Contributions

The survey contributes to the current knowledge base in several notable ways:

• Modular Summary: It delineates key modules of NL2SQL solutions that leverage LLMs, including encoding, decoding, prompt strategies, and intermediate NL representations. By doing so, it provides a granular insight into the mechanisms that underpin high-performing NL2SQL systems.
• Benchmark Review: The paper reviews a range of NL2SQL benchmarks, analyzing their characteristics and offering a statistical breakdown of various datasets, elucidating the impact of diverse benchmarks on modeling performance.
• Evaluation Metrics: Comprehensive analysis of widely used evaluation metrics and toolkits accentuates the importance of a rigorous evaluation for NL2SQL models. It proposes a valuable roadmap for optimizing LLMs for NL2SQL tasks and suggests a decision flow for selecting appropriate modules tailored to different scenarios.
• Open Problems: The paper identifies key open problems in the field, including the development of cost-effective NL2SQL methods, addressing ambiguous NL queries, and ensuring the trustworthiness and reliability of NL2SQL solutions.

Practical and Theoretical Implications

Practical Implications:

The extensive review of techniques and methodologies for NL2SQL provides practitioners with actionable guidance to optimize existing systems. The comprehensive evaluation framework helps practitioners implement more robust and efficient NL2SQL interfaces by systematically improving upon current shortcomings.

Theoretical Implications:

The emphasis on LLM's integration highlights emergent theoretical challenges and opportunities, such as exploring hybrid models combining LLMs and PLMs, and investigating advanced in-context learning. These aspects not only push the theoretical boundaries of NL2SQL but also optimize the practical deployment and computational efficiency of such systems.

Future Directions

The paper aptly speculates future developments in AI relevant to the NL2SQL domain. Potential research directions include:

• The refinement of execution-guided strategies to ensure query robustness and efficiency.
• Developing adaptive training data synthesis techniques to cover broader domains and scenarios.
• Integrating domain-specific knowledge to manage ambiguous NL queries effectively.
• Advancing explainability and debugging tools to make NL2SQL models more trustworthy and transparent.

Conclusion

This paper provides a meticulous review of the state of NL2SQL, particularly in the context of advancements driven by LLMs. It bridges the gap between natural language processing and database management systems, presenting a holistic view of challenges, solutions, and future research directions. Such a structured and comprehensive survey is pivotal for anyone in the field aiming to develop, refine, or implement NL2SQL models, guiding them through the myriad of considerations from data synthesis to error analysis and beyond.