Enhancing Numerical Reasoning with the Guidance of Reliable Reasoning
  Processes
Dingzirui Wang Longxu Dou Xuanliang Zhang Qingfu Zhu Wanxiang Che
Numerical reasoning is an essential ability for NLP systems to handle numeric
information. Recent research indicates that fine-tuning a small-scale model to
learn generating reasoning processes alongside answers can significantly
enhance performance. However, current methods have the limitation that most
methods generate reasoning processes with large language models (LLMs), which
are "unreliable" since such processes could contain information unrelated to
the answer. To address this limitation, we introduce Enhancing NumeriCal
reasOning with Reliable procEsses (Encore), which derives the reliable
reasoning process by decomposing the answer formula, ensuring which fully
supports the answer. Nevertheless, models could lack enough data to learn the
reasoning process generation adequately, since our method generates only one
single reasoning process for one formula. To overcome this difficulty, we
present a series of pre-training tasks to help models learn the reasoning
process generation with synthesized data. The experiments show that Encore
yields improvement on all five experimental datasets with an average of 1.8%,
proving the effectiveness of our method.