Pith. sign in

REVIEW 4 cited by

Natural SQL: Making SQL Easier to Infer from Natural Language Specifications

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2109.05153 v1 pith:UXEIZL4N submitted 2021-09-11 cs.CL

Yujian Gan , Xinyun Chen , Jinxia Xie , Matthew Purver , John R. Woodward , John Drake , Qiaofu Zhang This is my paper · ORCID
classification cs.CL
keywords natsqlnaturalqueriesdescriptionseasierexecutablelanguagemaking
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Addressing the mismatch between natural language descriptions and the corresponding SQL queries is a key challenge for text-to-SQL translation. To bridge this gap, we propose an SQL intermediate representation (IR) called Natural SQL (NatSQL). Specifically, NatSQL preserves the core functionalities of SQL, while it simplifies the queries as follows: (1) dispensing with operators and keywords such as GROUP BY, HAVING, FROM, JOIN ON, which are usually hard to find counterparts for in the text descriptions; (2) removing the need for nested subqueries and set operators; and (3) making schema linking easier by reducing the required number of schema items. On Spider, a challenging text-to-SQL benchmark that contains complex and nested SQL queries, we demonstrate that NatSQL outperforms other IRs, and significantly improves the performance of several previous SOTA models. Furthermore, for existing models that do not support executable SQL generation, NatSQL easily enables them to generate executable SQL queries, and achieves the new state-of-the-art execution accuracy.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

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

  1. ODIN: A NL2SQL Recommender to Handle Schema Ambiguity

    cs.DB 2025-05 conditional novelty 6.0 of 10

    ODIN uses schema masking, conformal-prediction filtering, and user-preference hints to return a small set of SQL queries that includes the correct one more often than baseline sampling on ambiguous-schema benchmarks.

  2. ExeSQL: Self-Taught Text-to-SQL Models with Execution-Driven Bootstrapping for SQL Dialects

    cs.CL 2025-05 conditional novelty 6.0 of 10

    Execution-driven bootstrapping, where a model generates SQL, executes it, and keeps only queries that run, lets a 7B model outperform GPT-4o on PostgreSQL, MySQL, and Oracle text-to-SQL benchmarks.

  3. 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.

  4. StreamLink: Large-Language-Model Driven Distributed Data Engineering System

    cs.DB 2025-05 conditional novelty 4.0 of 10

    A locally deployed LLM-based distributed data system converts natural language to SQL, and its fine-tuned Llama-3.1-8B model reaches 86.9% exact match and 89.7% execution accuracy on the Spider dev set.

Pith tools