Text-To-SQL
134 papers with code • 6 benchmarks • 14 datasets
Text-to-SQL is a task in natural language processing (NLP) where the goal is to automatically generate SQL queries from natural language text. The task involves converting the text input into a structured representation and then using this representation to generate a semantically correct SQL query that can be executed on a database.
( Image credit: SyntaxSQLNet )
Benchmarks
These leaderboards are used to track progress in Text-To-SQL
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Libraries
Use these libraries to find Text-To-SQL models and implementationsDatasets
Spider-Realistic
SParC
CoSQL
KaggleDBQA
SEDE
MultiSpider
Latest papers with no code
On Linearizing Structured Data in Encoder-Decoder Language Models: Insights from Text-to-SQL
Structured data, prevalent in tables, databases, and knowledge graphs, poses a significant challenge in its representation.
TrustSQL: A Reliability Benchmark for Text-to-SQL Models with Diverse Unanswerable Questions
To explore this aspect, we introduce TrustSQL, a new benchmark designed to assess the reliability of text-to-SQL models in both single-database and cross-database settings.
Retrieval augmented text-to-SQL generation for epidemiological question answering using electronic health records
Electronic health records (EHR) and claims data are rich sources of real-world data that reflect patient health status and healthcare utilization.
Schema-Aware Multi-Task Learning for Complex Text-to-SQL
Conventional text-to-SQL parsers are not good at synthesizing complex SQL queries that involve multiple tables or columns, due to the challenges inherent in identifying the correct schema items and performing accurate alignment between question and schema items.
Benchmarking the Text-to-SQL Capability of Large Language Models: A Comprehensive Evaluation
Then we formulate five evaluation tasks to comprehensively assess the performance of diverse methods across various LLMs throughout the Text-to-SQL process. Our study highlights the performance disparities among LLMs and proposes optimal in-context learning solutions tailored to each task.
DFIN-SQL: Integrating Focused Schema with DIN-SQL for Superior Accuracy in Large-Scale Databases
The task of converting natural language queries into SQL queries is intricate, necessitating a blend of precise techniques for an accurate translation.
Ar-Spider: Text-to-SQL in Arabic
The baselines demonstrate decent single-language performance on our Arabic text-to-SQL dataset, Ar-Spider, achieving 62. 48% for S2SQL and 65. 57% for LGESQL, only 8. 79% below the highest results achieved by the baselines when trained in English dataset.
SQL-CRAFT: Text-to-SQL through Interactive Refinement and Enhanced Reasoning
Modern LLMs have become increasingly powerful, but they are still facing challenges in specialized tasks such as Text-to-SQL.
Archer: A Human-Labeled Text-to-SQL Dataset with Arithmetic, Commonsense and Hypothetical Reasoning
We present Archer, a challenging bilingual text-to-SQL dataset specific to complex reasoning, including arithmetic, commonsense and hypothetical reasoning.
Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLM
Generating accurate SQL for user queries (text-to-SQL) is a long-standing problem since the generation of the SQL requires comprehending the query and database and retrieving the accurate data from the database accordingly.
