IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning
Zihang Xu Ziqing Yang Yiming Cui Shijin Wang
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.