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.

翻译：在机器阅读理解领域，现有系统已在SQuAD等多项任务中超越人类平均水平。然而，在逻辑推理方面仍任重道远。尽管已有一些应对方法被提出，但它们要么设计过于复杂，要么过度依赖外部结构。本文提出IDOL（指示器导向逻辑预训练）——一种简单易懂且高效的新型预训练任务，借助6类逻辑指示符和逻辑丰富的LGP（逻辑预训练）数据集，从逻辑层面强化预训练模型。IDOL在逻辑推理MRC领域最具代表性的两大基准ReClor和LogiQA上均达到最优性能，并被证实在保持GLUE任务测试中具有竞争力通用语言理解能力的同时，能够泛化至不同预训练模型及RACE、SQuAD 2.0等其他类型MRC基准。此外，在大语言模型时代初期，我们将其与ChatGPT等数个大型模型进行对比，发现IDOL仍展现出其优势。