  Table-based reasoning has shown remarkable progress in combining deep models
with discrete reasoning, which requires reasoning over both free-form natural
language (NL) questions and structured tabular data. However, previous
table-based reasoning solutions usually suffer from significant performance
degradation on huge evidence (tables). In addition, most existing methods
struggle to reason over complex questions since the required information is
scattered in different places. To alleviate the above challenges, we exploit
large language models (LLMs) as decomposers for effective table-based
reasoning, which (i) decompose huge evidence (a huge table) into sub-evidence
(a small table) to mitigate the interference of useless information for table
reasoning; and (ii) decompose complex questions into simpler sub-questions for
text reasoning. Specifically, we first use the LLMs to break down the evidence
(tables) involved in the current question, retaining the relevant evidence and
excluding the remaining irrelevant evidence from the huge table. In addition,
we propose a "parsing-execution-filling" strategy to alleviate the
hallucination dilemma of the chain of thought by decoupling logic and numerical
computation in each step. Extensive experiments show that our method can
effectively leverage decomposed evidence and questions and outperforms the
strong baselines on TabFact, WikiTableQuestion, and FetaQA datasets. Notably,
our model outperforms human performance for the first time on the TabFact
dataset.
