Related papers: Can neural networks do arithmetic? A survey on the elementary numerical skills of state-of-the-art deep learning models

Can neural networks do arithmetic? A survey on the elementary numerical skills of state-of-the-art deep learning models

URL: http://arxiv.org/abs/2303.07735v1
Date: Tue, 14 Mar 2023 09:30:52 GMT
Title: Can neural networks do arithmetic? A survey on the elementary numerical skills of state-of-the-art deep learning models
Authors: Alberto Testolin
Abstract summary: It is unclear whether deep learning models possess an elementary understanding of quantities and symbolic numbers. We critically examine the recent literature, concluding that even state-of-the-art architectures often fall short when probed with relatively simple tasks designed to test basic numerical and arithmetic knowledge.
Score: 0.424243593213882
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: Creating learning models that can exhibit sophisticated reasoning skills is one of the greatest challenges in deep learning research, and mathematics is rapidly becoming one of the target domains for assessing scientific progress in this direction. In the past few years there has been an explosion of neural network architectures, data sets, and benchmarks specifically designed to tackle mathematical problems, reporting notable success in disparate fields such as automated theorem proving, numerical integration, and discovery of new conjectures or matrix multiplication algorithms. However, despite these impressive achievements it is still unclear whether deep learning models possess an elementary understanding of quantities and symbolic numbers. In this survey we critically examine the recent literature, concluding that even state-of-the-art architectures often fall short when probed with relatively simple tasks designed to test basic numerical and arithmetic knowledge.

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