Reasoning with Transformer-based Models: Deep Learning, but Shallow Reasoning
doi:10.24432/C5W300
TL;DR
Recent years have seen impressive performance of transformer-based models on different natural language processing tasks. However, it is not clear to what degree the transformers can reason on natural language. To shed light on this question, this survey paper discusses the performance of transformers on different reasoning tasks, including mathematical reasoning, commonsense reasoning, and logical reasoning. We point out successes and limitations, of both empirical and theoretical nature.
Citation
@inproceedings{
helwe2021reasoning,
title={Reasoning with Transformer-based Models: Deep Learning, but Shallow Reasoning},
author={Chadi Helwe and Chlo{\'e} Clavel and Fabian M. Suchanek},
booktitle={3rd Conference on Automated Knowledge Base Construction},
year={2021},
url={https://openreview.net/forum?id=Ozp1WrgtF5_},
doi={10.24432/C5W300}
}