Mathematical Capabilities of ChatGPT
Simon Frieder, Luca Pinchetti, Alexis Chevalier, Ryan-Rhys Griffiths,, Tommaso Salvatori, Thomas Lukasiewicz, Philipp Christian Petersen, Julius, Berner

TL;DR
This study evaluates the mathematical reasoning capabilities of ChatGPT and GPT-4 on graduate-level datasets, revealing that while useful as a fact-finding tool, their overall performance is below that of graduate students.
Contribution
The paper introduces two new datasets, GHOSTS and miniGHOSTS, curated by mathematicians to assess language models on graduate-level mathematics and reasoning.
Findings
ChatGPT effectively queries mathematical facts and acts as a knowledge base.
GPT-4 performs well on undergraduate math but struggles with graduate-level problems.
Both models' performance is below that of an average graduate student.
Abstract
We investigate the mathematical capabilities of two iterations of ChatGPT (released 9-January-2023 and 30-January-2023) and of GPT-4 by testing them on publicly available datasets, as well as hand-crafted ones, using a novel methodology. In contrast to formal mathematics, where large databases of formal proofs are available (e.g., the Lean Mathematical Library), current datasets of natural-language mathematics, used to benchmark language models, either cover only elementary mathematics or are very small. We address this by publicly releasing two new datasets: GHOSTS and miniGHOSTS. These are the first natural-language datasets curated by working researchers in mathematics that (1) aim to cover graduate-level mathematics, (2) provide a holistic overview of the mathematical capabilities of language models, and (3) distinguish multiple dimensions of mathematical reasoning. These datasets…
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Taxonomy
TopicsArtificial Intelligence in Healthcare and Education · Topic Modeling · Explainable Artificial Intelligence (XAI)
MethodsBalanced Selection
