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Mathematical Capabilities of ChatGPT

Simon Frieder‚ Luca Pinchetti‚ Alexis Chevalier‚ Ryan−Rhys Griffiths‚ Tommaso Salvatori‚ Thomas Lukasiewicz‚ Philipp Christian Petersen and Julius Berner


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 test on 1636 human expert evaluations whether ChatGPT and GPT-4 can be helpful assistants to professional mathematicians by emulating use cases that arise in the daily professional activities of mathematicians. We benchmark the models on a range of fine-grained performance metrics. For advanced mathematics, this is the most detailed evaluation effort to date. We find that ChatGPT and GPT-4 can be used most successfully as mathematical assistants for querying facts, acting as mathematical search engines and knowledge base interfaces, achieving scores of 3.93, 3.97, and 4.56 (out of 5) for these tasks, respectively. GPT-4 can additionally be used for undergraduate-level mathematics but fails on graduate-level difficulty. Contrary to many positive reports in the media about GPT-4 and ChatGPT's exam-solving abilities (a potential case of selection bias), their overall mathematical performance is well below the level of a graduate student, achieving grades of 3.17, 3.22, and 3.80, respectively, on a selection of graduate-level textbooks. Hence, if you aim to use ChatGPT to pass a graduate-level math exam, you would be better off copying from your average peer!

Book Title
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 3‚ NeurIPS Datasets and Benchmarks 2023‚ December 2023