The goal of database question answering is to enable natural language querying of real-life
relational databases in diverse application domains. Recently, large-scale datasets such as …

We present a new text-to-SQL dataset for electronic health records (EHRs). The utterances
were collected from 222 hospital staff, including physicians, nurses, insurance review and …

Text-to-SQL allows experts to use databases without in-depth knowledge of them. However,
real-world tasks have both query and data ambiguities. Most works on Text-to-SQL focused …

With the future striving toward data-centric decision-making, seamless access to databases
is of utmost importance. There is extensive research on creating an efficient text-to-sql …

Incorporating unanswerable questions into EHR QA systems is crucial for testing the
trustworthiness of a system, as providing non-existent responses can mislead doctors in …

Text-to-SQL enables users to interact with databases using natural language, simplifying the
retrieval and synthesis of information. Despite the remarkable success of large language …

We develop NL2INTERFACE to explore the potential of generating usable interactive multi-
visualization interfaces from natural language queries. With NL2INTERFACE, users can …

Fine-tuning large language models (LLMs) for specific domain tasks has achieved great
success in Text-to-SQL tasks. However, these fine-tuned models often face challenges with …

In the ever-expanding realm of data-driven exploration and decision-making, the ability to
harness the power of natural language to interact with databases has become an …

Previous text-to-SQL datasets and systems have primarily focused on user questions with
clear intentions that can be answered. However, real user questions can often be …