Text-To-SQL
136 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
AraSpider: Democratizing Arabic-to-SQL
The results showed that using back translation significantly improved the performance of both ChatGPT 3. 5 and SQLCoder models, which are considered top performers on the Spider dataset.
Analyzing the Effectiveness of Large Language Models on Text-to-SQL Synthesis
This study investigates various approaches to using Large Language Models (LLMs) for Text-to-SQL program synthesis, focusing on the outcomes and insights derived.
MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL
Our framework comprises a core decomposer agent for Text-to-SQL generation with few-shot chain-of-thought reasoning, accompanied by two auxiliary agents that utilize external tools or models to acquire smaller sub-databases and refine erroneous SQL queries.
LLM-SQL-Solver: Can LLMs Determine SQL Equivalence?
Judging the equivalence between two SQL queries is a fundamental problem with many practical applications in data management and SQL generation (i. e., evaluating the quality of generated SQL queries in text-to-SQL task).
DBCopilot: Scaling Natural Language Querying to Massive Databases
tshu-w/dbcopilot
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Text-to-SQL simplifies database interactions by enabling non-experts to convert their natural language (NL) questions into Structured Query Language (SQL) queries.
CRUSH4SQL: Collective Retrieval Using Schema Hallucination For Text2SQL
imayk/crush4sql
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Standard dense retrieval techniques are inadequate for schema subsetting of a large structured database, where the correct semantics of retrieval demands that we rank sets of schema elements rather than individual elements.
SQLformer: Deep Auto-Regressive Query Graph Generation for Text-to-SQL Translation
However, some of its key hurdles include domain generalisation, which is the ability to adapt to previously unseen databases, and alignment of natural language questions with the corresponding SQL queries.
ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thought
Recently Large Language Models (LLMs) have been proven to have strong abilities in various domains and tasks.
TUR2SQL: A Cross-Domain Turkish Dataset For Text-to-SQL
The field of converting natural language into corresponding SQL queries using deep learning techniques has attracted significant attention in recent years.
Benchmarking and Improving Text-to-SQL Generation under Ambiguity
Research in Text-to-SQL conversion has been largely benchmarked against datasets where each text query corresponds to one correct SQL.
