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    <abstract>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 LoGic Pre-training (LGP). 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.</abstract>
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%0 Conference Proceedings
%T IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning
%A Xu, Zihang
%A Yang, Ziqing
%A Cui, Yiming
%A Wang, Shijin
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F xu-etal-2023-idol
%X 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 LoGic Pre-training (LGP). 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.
%R 10.18653/v1/2023.findings-acl.513
%U https://aclanthology.org/2023.findings-acl.513
%U https://doi.org/10.18653/v1/2023.findings-acl.513
%P 8099-8111

Markdown (Informal)

[IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning](https://aclanthology.org/2023.findings-acl.513) (Xu et al., Findings 2023)

• IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning (Xu et al., Findings 2023)

ACL

• Zihang Xu, Ziqing Yang, Yiming Cui, and Shijin Wang. 2023. IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning. In Findings of the Association for Computational Linguistics: ACL 2023, pages 8099–8111, Toronto, Canada. Association for Computational Linguistics.