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The MATH dataset consists of 12,500 problems taken from various high school mathematics competitions, inform Shraddha Goled, journalist with a postgraduate degree in computer network engineering.
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Do Large Machine Learning Models Struggle At Math Photo: Analytics India Magazine |
In 1960, Nobel Laureate and American physicist Eugene Wigner wrote
about the ‘unreasonable effectiveness of mathematics in natural
sciences’. Mathematics is called the language of nature for a reason.
That’s why the ‘Is math invented or discovered?’ debate never gets old.
Mathematics exerts its influence on literally every field.
Mathematics is also the building block of machine learning models. ML practitioners use mathematics to analyse a problem, pick out better heuristics, and club both to generate an answer. Despite the critical role mathematics plays in machine learning, even state-of-art models struggle at maths.
A new study by the researchers at the University of California, Berkeley, have now introduced the MATH dataset. The team said the dataset provides a detailed assessment of a model’s mathematical ability across difficulties and subjects...
The researchers found simply increasing the amount of training time, and the parameters proved extremely costly, although they did improve performance in a few cases. The researchers have open-sourced both MATH and AMPS to encourage and facilitate further research in this direction.
