  Tabular data analysis is crucial in various fields, and large language models
show promise in this area. However, current research mostly focuses on
rudimentary tasks like Text2SQL and TableQA, neglecting advanced analysis like
forecasting and chart generation. To address this gap, we developed the
Text2Analysis benchmark, incorporating advanced analysis tasks that go beyond
the SQL-compatible operations and require more in-depth analysis. We also
develop five innovative and effective annotation methods, harnessing the
capabilities of large language models to enhance data quality and quantity.
Additionally, we include unclear queries that resemble real-world user
questions to test how well models can understand and tackle such challenges.
Finally, we collect 2249 query-result pairs with 347 tables. We evaluate five
state-of-the-art models using three different metrics and the results show that
our benchmark presents introduces considerable challenge in the field of
tabular data analysis, paving the way for more advanced research opportunities.
