With the great success of ChatGPT, the research of large language models has
become increasingly popular. However, the models have several limitations, such
as toxicity and pool performance of arithmetic solving. Meanwhile, LLM may have
some potential abilities that have yet to be exploited. In this paper, we
choose a different way to enhance the arithmetic ability of LLM. We propose to
train LLM to generate a postfix expression related to the arithmetic problem
and incorporate it with small pretrained models. Moreover, this small model
transfers the token embeddings into real dense numbers and invokes native
functions of a deep learning platform to get the correct answer. To generate
the final result, we propose prompt injection for adding the result outputs by
the small model to LLM. This work provides different ways of thinking, training
and using a language model. The codes and models will be released at
https://github.com/eteced/arithmetic_finetuning_v1.

通过训练语言模型生成与算数问题相关的后缀表达式，并将其与小型预训练模型结合，将令牌嵌入转化为真实稠密数字，通过深度学习平台的本地函数获得正确答案。最后，通过提示注入将小型模型的结果输出添加到语言模型中，从而增强了语言模型的算数能力。