  Table reasoning is a challenging task that requires understanding both
natural language questions and structured tabular data. Large language models
(LLMs) have shown impressive capabilities in natural language understanding and
generation, but they often struggle with large tables due to their limited
input length. In this paper, we propose TabSQLify, a novel method that
leverages text-to-SQL generation to decompose tables into smaller and relevant
sub-tables, containing only essential information for answering questions or
verifying statements, before performing the reasoning task. In our
comprehensive evaluation on four challenging datasets, our approach
demonstrates comparable or superior performance compared to prevailing methods
reliant on full tables as input. Moreover, our method can reduce the input
context length significantly, making it more scalable and efficient for
large-scale table reasoning applications. Our method performs remarkably well
on the WikiTQ benchmark, achieving an accuracy of 64.7%. Additionally, on the
TabFact benchmark, it achieves a high accuracy of 79.5%. These results surpass
other LLM-based baseline models on gpt-3.5-turbo (chatgpt). TabSQLify can
reduce the table size significantly alleviating the computational load on LLMs
when handling large tables without compromising performance.
