Leading models for the text-to-SQL task heavily rely on proprietary Large Language Models
(LLMs), posing concerns over data privacy. Closing the performance gap between small …

Large Language Models (LLMs) have emerged as powerful tools for Text-to-SQL tasks,
exhibiting remarkable reasoning capabilities. Different from tasks such as math word …

The capability gap between open-source and closed-source large language models (LLMs)
remains a challenge in text-to-SQL tasks. In this paper, we introduce a synthetic data …

Text-to-SQL parsing, which aims at converting natural language instructions into executable
SQLs, has gained increasing attention in recent years. In particular, GPT-4 and Claude-2 …

This study explores text-to-SQL parsing by leveraging the powerful reasoning capabilities of
large language models (LLMs). Despite recent advancements, existing LLM-based methods …

Language models have shown promising performance on the task of translating natural
language questions into SQL queries (Text-to-SQL). However, most of the state-of-the-art …

The increasing volume of data in relational databases and the expertise needed for writing
SQL queries pose challenges for users to access and analyze data. Text-to-SQL (Text2SQL) …

Large language models (LLMs) have emerged as a new paradigm for Text-to-SQL task.
However, the absence of a systematical benchmark inhibits the development of designing …

In-context learning of large-language models (LLMs) has achieved remarkable success in
the field of natural language processing, while extensive case studies reveal that the single …

Text-to-SQL, the process of translating natural language into Structured Query Language
(SQL), represents a transformative application of large language models (LLMs), potentially …