  The potential for improvements brought by Large Language Models (LLMs) in
Text-to-SQL systems is mostly assessed on monolingual English datasets.
However, LLMs' performance for other languages remains vastly unexplored. In
this work, we release the StatBot.Swiss dataset, the first bilingual benchmark
for evaluating Text-to-SQL systems based on real-world applications. The
StatBot.Swiss dataset contains 455 natural language/SQL-pairs over 35 big
databases with varying level of complexity for both English and German.
  We evaluate the performance of state-of-the-art LLMs such as GPT-3.5-Turbo
and mixtral-8x7b-instruct for the Text-to-SQL translation task using an
in-context learning approach. Our experimental analysis illustrates that
current LLMs struggle to generalize well in generating SQL queries on our novel
bilingual dataset.
