Text-to-SQL parsing is an essential and challenging task. The goal of text-to-SQL parsing is
to convert a natural language (NL) question to its corresponding structured query language …

There is currently a significant gap between the performance of fine-tuned models and
prompting approaches using Large Language Models (LLMs) on the challenging task of text …

To bridge the gap between users and data, numerous text-to-SQL systems have been
developed that allow users to pose natural language questions over relational databases …

Answering complex factual questions has drawn a lot of attention. Researchers leverage
various data sources to support complex QA, such as unstructured texts, structured …

Continual table semantic parsing aims to train a parser on a sequence of tasks, where each
task requires the parser to translate natural language into SQL based on task-specific tables …

Natural language to SQL (NL2SQL) techniques provide a convenient interface to access
databases, especially for non-expert users, to conduct various data analytics. Existing …

Large language models have recently advanced the state of the art on many natural
language processing benchmarks. The newest generation of models can be applied to a …

Large Language Models (LLMs) have emerged as a powerful tool in advancing the Text-to-
SQL task, significantly outperforming traditional methods. Nevertheless, as a nascent …

Existing text-to-SQL semantic parsers are typically designed for particular settings such as
handling queries that span multiple tables, domains, or turns which makes them ineffective …

Due to the development of pre-trained language models, automated code generation
techniques have shown great promise in recent years. However, the generated code will not …