Text-To-SQL

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

10 leaderboard tables shown for this task, 10 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

21 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 209 papers with code (424 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Non-local Neural Networks
  21 Nov 2017 32 repositories listed Syntology ran 3 of 4 samples · 1 unverified · 4 pointer-only (licence)
  Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time.
• Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
  31 Aug 2017 15 repositories listed
  A significant amount of the world's knowledge is stored in relational databases.
• SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning
  13 Nov 2017 13 repositories listed Syntology ran 4 of 19 samples · 15 unverified
  Existing state-of-the-art approaches rely on reinforcement learning to reward the decoder when it generates any of the equivalent serializations.
• Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task
  24 Sep 2018 6 repositories listed Syntology ran 7 of 8 samples · 1 unverified · 3 pointer-only (licence)
  We define a new complex and cross-domain semantic parsing and text-to-SQL task where different complex SQL queries and databases appear in train and test sets.
• Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task
  24 Sep 2018 6 repositories listed Syntology ran 7 of 8 samples · 1 unverified · 3 pointer-only (licence)
  We define a new complex and cross-domain semantic parsing and text-to-SQL task where different complex SQL queries and databases appear in train and test sets.
• A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL
  13 Nov 2024 5 repositories listed
  On the other hand, we implement the ICL approach with an example selection method based on named entity recognition to prevent overemphasis on entities.
• A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL
  13 Nov 2024 5 repositories listed
  On the other hand, we implement the ICL approach with an example selection method based on named entity recognition to prevent overemphasis on entities.
• Content Enhanced BERT-based Text-to-SQL Generation
  16 Oct 2019 5 repositories listed
  We present a simple methods to leverage the table content for the BERT-based model to solve the text-to-SQL problem.
• Content Enhanced BERT-based Text-to-SQL Generation
  16 Oct 2019 5 repositories listed
  We present a simple methods to leverage the table content for the BERT-based model to solve the text-to-SQL problem.
• Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation
  20 May 2019 5 repositories listed Syntology ran 0 of 20 samples · 20 unverified · 12 pointer-only (licence)
  We present a neural approach called IRNet for complex and cross-domain Text-to-SQL.
• Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation
  20 May 2019 5 repositories listed Syntology ran 0 of 20 samples · 20 unverified · 12 pointer-only (licence)
  We present a neural approach called IRNet for complex and cross-domain Text-to-SQL.
• XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL
  7 Jul 2025 4 repositories listed Syntology ran 0 of 10 samples · 10 unverified
  To leverage the advantages of LLM in addressing challenges in the Text-to-SQL task, we present XiYan-SQL, an innovative framework effectively generating and utilizing multiple SQL candidates.
• XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL
  7 Jul 2025 4 repositories listed Syntology ran 0 of 10 samples · 10 unverified
  To leverage the advantages of LLM in addressing challenges in the Text-to-SQL task, we present XiYan-SQL, an innovative framework effectively generating and utilizing multiple SQL candidates.
• RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers
  10 Nov 2019 4 repositories listed Syntology ran 4 of 7 samples · 3 unverified
  The generalization challenge lies in (a) encoding the database relations in an accessible way for the semantic parser, and (b) modeling alignment between database columns and their mentions in a given query.
• RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers
  10 Nov 2019 4 repositories listed Syntology ran 4 of 7 samples · 3 unverified
  The generalization challenge lies in (a) encoding the database relations in an accessible way for the semantic parser, and (b) modeling alignment between database columns and their mentions in a given query.
• SParC: Cross-Domain Semantic Parsing in Context
  5 Jun 2019 4 repositories listed Syntology ran 4 of 4 samples · 0 unverified · 3 pointer-only (licence)
  The best model obtains an exact match accuracy of 20.
• SParC: Cross-Domain Semantic Parsing in Context
  5 Jun 2019 4 repositories listed Syntology ran 4 of 4 samples · 0 unverified · 3 pointer-only (licence)
  The best model obtains an exact match accuracy of 20.
• Natural SQL: Making SQL Easier to Infer from Natural Language Specifications
  11 Sep 2021 3 repositories listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)
  Addressing the mismatch between natural language descriptions and the corresponding SQL queries is a key challenge for text-to-SQL translation.
• Natural SQL: Making SQL Easier to Infer from Natural Language Specifications
  11 Sep 2021 3 repositories listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)
  Addressing the mismatch between natural language descriptions and the corresponding SQL queries is a key challenge for text-to-SQL translation.
• PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models
  10 Sep 2021 3 repositories listed Syntology ran 4 of 7 samples · 3 unverified
  Large pre-trained language models for textual data have an unconstrained output space; at each decoding step, they can produce any of 10, 000s of sub-word tokens.
• PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models
  10 Sep 2021 3 repositories listed Syntology ran 4 of 7 samples · 3 unverified
  Large pre-trained language models for textual data have an unconstrained output space; at each decoding step, they can produce any of 10, 000s of sub-word tokens.
• Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training
  18 Dec 2020 3 repositories listed
  Most recently, there has been significant interest in learning contextual representations for various NLP tasks, by leveraging large scale text corpora to train large neural language models with self-supervised learning…
• Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training
  18 Dec 2020 3 repositories listed
  Most recently, there has been significant interest in learning contextual representations for various NLP tasks, by leveraging large scale text corpora to train large neural language models with self-supervised learning…
• Semantic Evaluation for Text-to-SQL with Distilled Test Suites
  6 Oct 2020 3 repositories listed Syntology ran 5 of 5 samples · 0 unverified · 3 pointer-only (licence)
  We propose test suite accuracy to approximate semantic accuracy for Text-to-SQL models.
• Semantic Evaluation for Text-to-SQL with Distilled Test Suites
  6 Oct 2020 3 repositories listed Syntology ran 5 of 5 samples · 0 unverified · 3 pointer-only (licence)
  We propose test suite accuracy to approximate semantic accuracy for Text-to-SQL models.
• CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases
  11 Sep 2019 3 repositories listed Syntology ran 1 of 11 samples · 10 unverified
  We present CoSQL, a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems.
• CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases
  11 Sep 2019 3 repositories listed Syntology ran 1 of 11 samples · 10 unverified
  We present CoSQL, a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems.
• Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions
  2 Sep 2019 3 repositories listed
  We focus on the cross-domain context-dependent text-to-SQL generation task.
• Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions
  2 Sep 2019 3 repositories listed
  We focus on the cross-domain context-dependent text-to-SQL generation task.
• Towards Automated Cross-domain Exploratory Data Analysis through Large Language Models
  10 Dec 2024 2 repositories listed
  Exploratory data analysis (EDA), coupled with SQL, is essential for data analysts involved in data exploration and analysis.

Syntology lines on 20 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.