Tables are powerful and popular tools for organizing and manipulating data. A vast number
of tables can be found on the Web, which represent a valuable knowledge resource. The …

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 …

Automated question answering has been a topic of research and development since the
earliest AI applications. Computing power has increased since the first such systems were …

When translating natural language questions into SQL queries to answer questions from a
database, contemporary semantic parsing models struggle to generalize to unseen …

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 …

We present Spider, a large-scale, complex and cross-domain semantic parsing and text-to-
SQL dataset annotated by 11 college students. It consists of 10,181 questions and 5,693 …

A significant amount of the world's knowledge is stored in relational databases. However,
the ability for users to retrieve facts from a database is limited due to a lack of understanding …

In this paper, we present a natural language code synthesis tool, GenLine, backed by 1) a
large generative language model and 2) a set of task-specific prompts that create or change …

This work aims to tackle the challenging heterogeneous graph encoding problem in the text-
to-SQL task. Previous methods are typically node-centric and merely utilize different weight …

Semantic parsing aims at mapping natural language to machine interpretable meaning
representations. Traditional approaches rely on high-quality lexicons, manually-built …