Drori et al. (2022) report that "A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level ... [It] automatically answers 81\% of university-level mathematics problems." The system they describe is indeed impressive; however, the above description is very much overstated. The work of solving the problems is done, not by a neural network, but by the symbolic algebra package Sympy. Problems of various formats are excluded from consideration. The so-called "explanations" are just rewordings of lines of code. Answers are marked as correct that are not in the form specified in the problem. Most seriously, it seems that in many cases the system uses the correct answer given in the test corpus to guide its path to solving the problem.
翻译:Drori等人(2022年)报告说,“神经网络通过编程合成和在人类一级微小的学习解决、解释和产生大学数学问题......[它]自动解决大学一级数学问题81 ⁇ 。”他们描述的系统确实令人印象深刻;但是,上述描述非常多。解决问题的工作不是由神经网络完成,而是由象征性的代数包Sympy完成。各种格式的问题都被排除在考虑之外。所谓的“解释”只是对代码线的重新措辞。答案被标为正确,并不是问题的具体形式。最严重的是,在很多情况下,系统使用测试中给出的正确答案来引导其解决问题的道路。