Claim · 2021-03-05 · publication · Stated in source · Supported

The paper introduces MATH, described as a dataset of 12,500 competition mathematics problems for measuring mathematical problem solving in machine learning models.

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What it connects

Measuring Mathematical Problem Solving With the MATH Dataset → states its contribution → The paper introduces MATH, described as a dataset of 12,500 competition mathematics problems for measuring mathematical problem solving in machine learning models.

Where it appears

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Claim and evidence

Measuring Mathematical Problem Solving With the MATH Datasetstates its contribution ↓The paper introduces MATH, described as a dataset of 12,500 competition mathematics problems for measuring mathematical problem solving in machine learning models.

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2021-03-05 · publication Records dated 2021 →

Scope and limitsThe paper's own description of its contribution; not an assessment of it.

The evidence 01

primary sourceSupports the claim

Measuring Mathematical Problem Solving With the MATH Dataset

arXiv · 2021-03-05

Cite the original source

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Source excerpt

“To measure this ability in machine learning models, we introduce MATH, a new dataset of 12, 500 challenging competition mathematics problems.”

Location in source arXiv v1, PDF p. 1, abstract

Machine-checked · 2026-10-31

Source details and check notes

Authors: Dan Hendrycks; Collin Burns; Saurav Kadavath; Akul Arora; Steven Basart; Eric Tang; Dawn Song; Jacob Steinhardt
Version: 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.
Version date: 2021-03-05
Canonical address: Open the canonical source ↗
Accessed: 2026-10-28
Language: English (en)
Attribution: Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, Jacob Steinhardt, “Measuring Mathematical Problem Solving With the MATH Dataset”, 2021-03-05.
Reuse: No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution.
Check notes: Candidate pack passage, key papers (research/key-papers). The quotation (21 words) matched the text retrieved for the candidate pack on 2026-10-28 (research/key-papers/verification.json). Matching ignored differences of whitespace, case, quotation marks, dashes and line-break hyphens, so the source’s typography can differ. No human editorial review.

Status and meaning

Editorial status. Not reviewed by an editor. An exploratory record that no editor has accepted. This status does not say whether a person has read any linked passage. Each evidence record above shows how it was checked.

Relation. A paper or other publication describes its own contribution, quoted from the expression the record names, usually its abstract or introduction. The record is the document’s own description, not an assessment: a claim of priority or novelty it contains is the document’s claim, flagged as such, and says nothing about earlier or later work.

Date. Date of the arXiv version quoted, submitted 5 March 2021 (UTC); later versions may word it differently.

Record ID kp-states-measuring-mathematical-problem-solving-math-2021-1