MIT researchers have developed a machine learning model that can answer university-level math problems in human-level seconds

Unlike humans, machine learning models find it incredibly challenging to tackle problems involving differential equations, linear algebra, and multivariable calculus. Even the most advanced models can only answer math problems at the elementary or middle school level, and they don’t always give the right answers. A multidisciplinary research team at MIT has created a neural network model that can quickly and accurately answer college-level arithmetic problems. The model can also automatically explain solutions in university math courses and quickly create new problems. University students were then given the computer-generated test questions and could not tell whether the questions were created by an algorithm or a human. The study was also published in the Proceedings of the National Academy of Sciences.

The researchers believe their work can be used to accelerate course content creation for massively-in-the-life courses and massive open online courses (MOOCs) with thousands of students. The program can also serve as an automated tutor that shows students how to solve college math problems. The team believes that by helping teachers understand the relationship between courses and their prerequisites, their approach has the potential to improve higher education. For more than two years, the model has been continuously developed. Early on, the researchers saw that models pre-trained only with text could not provide high accuracy on high school math problems. In contrast, those using graphical neural networks could, but would require longer training periods.

The scientists then experienced a “eureka” moment. They used program synthesis and multi-shot learning to convert questions from undergraduate mathematics courses at well-known universities that the model had never encountered before into programming tasks. The researchers added an additional “fine-tuning” stage before feeding these programming tasks to a neural network. The pre-trained neural network used, Codex, was “fine-tuned” on both the text and the code. The pre-trained model was trained on data containing millions of lines of code and natural language words, allowing it to understand the relationship between text and code. With just a few examples of question code, the model can now convert a text question into code and then execute the code to provide an answer because it can recognize different relationships between text and code. This method showed a huge improvement in accuracy, from 8 to 80 percent. By giving the neural network a set of arithmetic problems on a given topic and then asking it to come up with a new challenge, the researchers also used their model to generate queries. We also looked at these computer-generated questions by showing them to students. Students gave human- and machine-generated questions comparable ratings for difficulty level and suitability for the course because they could not differentiate between human- and algorithm-generated questions.

The team makes it clear that their efforts are aimed at paving the way for people to start using machine learning to solve more challenging problems, rather than replacing human professors. While the team is delighted with the results of their strategy, there are a few drawbacks they need to overcome. Due to the computational complexity, the model cannot answer questions with a visual component and cannot solve computationally intractable problems. Along with overcoming these hurdles, they want to build the model to hundreds of courses so that it can improve automation and offer insights into course design and curriculum.

This Article is written as a research summary article by Marktechpost Staff based on the research paper 'A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level'. All Credit For This Research Goes To Researchers on This Project. Checkout the paper and reference article.

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