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Measuring Mathematical Problem Solving With the MATH Dataset

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Collin Burns · Dan Hendrycks · Jacob Steinhardt · Saurav Kadavath

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Published 2021-03-05 authorship evidence authorship evidence authorship evidence authorship evidence

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Measuring Mathematical Problem Solving With the MATH Dataset arXiv · published 2021-03-05 · arXiv abstract page, retrieved for the candidate pack on 2026-10-28 (SHA-256 fb50a593009b3544a30578dbb2f91471d3c66c2a246dd4745c5d735075cdbc35). Submission history as arXiv records it (UTC): v1 5 March 2021; v2 8 November 2021. The page serves the latest version; each version is a separate expression, and only the one a record names was read.
Measuring Mathematical Problem Solving With the MATH Dataset arXiv · published 2021-03-05 · arXiv:2103.03874v1, submitted 5 March 2021 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 639e4e54f93910b9125ac72ea2e7bd96bde3e1934898f599b4e3898d743975e3). The author list is the byline as printed on the first page.

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In its own wordsThe paper introduces MATH, described as a dataset of 12,500 competition mathematics problems for measuring mathematical problem solving in machine learning models.“To measure this ability in machine learning models, we introduce MATH, a new dataset of 12, 500 challenging competition mathematics problems.” arXiv v1, PDF p. 1, abstract2021-03-05 · publication · Stated in source · Supported Inspect claim & evidence ↗