Text-To-SQL
129 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 )
Libraries
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Latest papers
CodeS: Towards Building Open-source Language Models for Text-to-SQL
To address the limitations, we introduce CodeS, a series of pre-trained language models with parameters ranging from 1B to 15B, specifically designed for the text-to-SQL task.
Understanding the Effects of Noise in Text-to-SQL: An Examination of the BIRD-Bench Benchmark
This study provides an in-depth analysis of the distribution and types of noise in the widely used BIRD-Bench benchmark and the impact of noise on models.
When is Tree Search Useful for LLM Planning? It Depends on the Discriminator
In this paper, we examine how large language models (LLMs) solve multi-step problems under a language agent framework with three components: a generator, a discriminator, and a planning method.
Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow Paradigm
To improve the contextual learning capabilities of LLMs in text-to-SQL, a workflow paradigm method is proposed, aiming to enhance the attention and problem-solving scope of LLMs through decomposition.
Improving Generalization in Semantic Parsing by Increasing Natural Language Variation
Text-to-SQL semantic parsing has made significant progress in recent years, with various models demonstrating impressive performance on the challenging Spider benchmark.
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
Text-to-SQL simplifies database interactions by enabling non-experts to convert their natural language (NL) questions into Structured Query Language (SQL) queries.