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 …

Text-to-SQL has attracted attention from both the natural language processing and database
communities because of its ability to convert the semantics in natural language into SQL …

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 …

We introduce DS-1000, a code generation benchmark with a thousand data science
problems spanning seven Python libraries, such as Numpy and Pandas. Compared to prior …

One of the recent best attempts at Text-to-SQL is the pre-trained language model. Due to the
structural property of the SQL queries, the seq2seq model takes the responsibility of parsing …

Many recent approaches to natural language tasks are built on the remarkable abilities of
large language models. Large language models can perform in-context learning, where they …

Large pre-trained language models for textual data have an unconstrained output space; at
each decoding step, they can produce any of 10,000 s of sub-word tokens. When fine-tuned …

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 …

This paper presents the first comprehensive analysis of ChatGPT's Text-to-SQL ability. Given
the recent emergence of large-scale conversational language model ChatGPT and its …