In the field of machine reading comprehension (MRC), existing systems have
surpassed the average performance of human beings in many tasks like SQuAD.
However, there is still a long way to go when it comes to logical reasoning.
Although some methods for it have been put forward, they either are designed in
a quite complicated way or rely too much on external structures. In this paper,
we proposed IDOL (InDicator-Oriented Logic Pre-training), an easy-to-understand
but highly effective further pre-training task which logically strengthens the
pre-trained models with the help of 6 types of logical indicators and a
logically rich dataset LGP (LoGic Pre-training). IDOL achieves state-of-the-art
performance on ReClor and LogiQA, the two most representative benchmarks in
logical reasoning MRC, and is proven to be capable of generalizing to different
pre-trained models and other types of MRC benchmarks like RACE and SQuAD 2.0
while keeping competitive general language understanding ability through
testing on tasks in GLUE. Besides, at the beginning of the era of large
language models, we take several of them like ChatGPT into comparison and find
that IDOL still shows its advantage.

本文提出了一种易于理解但非常有效的进一步预训练任务，IDOL（InDicator-Oriented Logic Pre-training），通过 6 种逻辑指示符和一个逻辑丰富的数据集 LGP（LoGic Pre-training）在逻辑上增强了预训练模型，使其在逻辑推理 MRC 领域的两个代表性基准测试 ReClor 和 LogiQA 上取得了最先进的性能，并被证明能够概括到不同的预训练模型和其他类型的 MRC 基准测试中。