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A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions

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arxiv 2208.13629 v1 pith:VMC2GFU3 submitted 2022-08-29 cs.CL

Bowen Qin , Binyuan Hui , Lihan Wang , Min Yang , Jinyang Li , Binhua Li , Ruiying Geng , Rongyu Cao
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Jian Sun Luo Si Fei Huang Yongbin Li
This is my paper · ORCID
classification cs.CL
keywords parsingtext-to-sqllanguagemodelstaskdeepdirectionsfuture
verification ladder T0 review T1 audit T2 compute T3 formal
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Text-to-SQL parsing is an essential and challenging task. The goal of text-to-SQL parsing is to convert a natural language (NL) question to its corresponding structured query language (SQL) based on the evidences provided by relational databases. Early text-to-SQL parsing systems from the database community achieved a noticeable progress with the cost of heavy human engineering and user interactions with the systems. In recent years, deep neural networks have significantly advanced this task by neural generation models, which automatically learn a mapping function from an input NL question to an output SQL query. Subsequently, the large pre-trained language models have taken the state-of-the-art of the text-to-SQL parsing task to a new level. In this survey, we present a comprehensive review on deep learning approaches for text-to-SQL parsing. First, we introduce the text-to-SQL parsing corpora which can be categorized as single-turn and multi-turn. Second, we provide a systematical overview of pre-trained language models and existing methods for text-to-SQL parsing. Third, we present readers with the challenges faced by text-to-SQL parsing and explore some potential future directions in this field.

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Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation

    cs.CL 2025-06 conditional novelty 6.0 of 10

    SEED automatically generates evidence from database schemas, descriptions, and sampled values, improving text-to-SQL accuracy in no-evidence settings.

  2. Automatic Metadata Extraction for Text-to-SQL

    cs.DB 2025-05 conditional novelty 6.0 of 10

    LLM-generated summaries of database profiling statistics outperform human-written metadata for text-to-SQL on BIRD, and a SQL-generation-based schema linker adds further gains.

  3. LogicCat: A Chain-of-Thought Text-to-SQL Benchmark for Complex Reasoning

    cs.CL 2025-05 conditional novelty 5.0 of 10

    The authors built a 4,038-question text-to-SQL benchmark with chain-of-thought annotations and found that state-of-the-art models reach only 33.20% execution accuracy.

  4. CHS-SQL: A Text-to-SQL approach based on Confidence-Guided Heuristic Search Schema Linking process

    cs.AI 2026-06 conditional novelty 4.0 of 10

    CHS-SQL combines beam search with model-internal-confidence filtering in schema linking to improve small-model text-to-SQL accuracy on Spider and BIRD.

  5. Interactive Text-to-SQL via Expected Information Gain for Disambiguation

    cs.DB 2025-07 reject novelty 4.0 of 10

    An interactive text-to-SQL framework selects clarification questions by expected information gain over a distribution of candidate SQL queries.

  6. LLM-Driven Data Generation and a Novel Soft Metric for Evaluating Text-to-SQL in Aviation MRO

    cs.DB 2025-06 conditional novelty 4.0 of 10

    Introduces an F1-based soft metric for text-to-SQL evaluation and an LLM-driven data generation pipeline, tested on an aviation MRO database.

  7. Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities

    cs.CL 2025-05 conditional novelty 4.0 of 10

    A systematic review organizing LLM-based text-to-SQL methods into pre-processing, in-context learning, fine-tuning, and post-processing paradigms, with a catalog of datasets, metrics, challenges, and future directions.

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