  Existing auto-regressive pre-trained language models (PLMs) like T5 and BART,
have been well applied to table question answering by UNIFIEDSKG and TAPEX,
respectively, and demonstrated state-of-the-art results on multiple benchmarks.
However, auto-regressive PLMs are challenged by recent emerging numerical
reasoning datasets, such as TAT-QA, due to the error-prone implicit
calculation. In this paper, we present TaCube, to pre-compute
aggregation/arithmetic results for the table in advance, so that they are handy
and readily available for PLMs to answer numerical reasoning questions. TaCube
systematically and comprehensively covers a collection of computational
operations over table segments. By simply concatenating TaCube to the input
sequence of PLMs, it shows significant experimental effectiveness. TaCube
promotes the F1 score from 49.6% to 66.2% on TAT-QA and achieves new
state-of-the-art results on WikiTQ (59.6% denotation accuracy). TaCube's
improvements on numerical reasoning cases are even more notable: on TAT-QA,
TaCube promotes the exact match accuracy of BART-large by 39.6% on sum, 52.5%
on average, 36.6% on substraction, and 22.2% on division. We believe that
TaCube is a general and portable pre-computation solution that can be
potentially integrated to various numerical reasoning frameworks
