<div class="csl-bib-body">
<div class="csl-entry">Frieder, S., Olšák, M., Berner, J., & Lukasiewicz, T. (2024). The IMO Small Challenge: Not-Too-Hard Olympiad Math Datasets for LLMs. In <i>The Second Tiny Papers Track at ICLR 2024</i>. The Twelfth International Conference on Learning Representations (ICLR 2024), Wien, Austria. http://hdl.handle.net/20.500.12708/210292</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/210292
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dc.description.abstract
We introduce the IMO Small Challenge (IMOSC), as opposed to the IMO Grand Challenge: A text-only, natural-language dataset consisting of mathematical problems from various mathematical competitions. The IMOSC dataset exceeds the difficulty level of current datasets that are widely used for LLM evaluation, such as the MATH dataset, while not being too challenging for the current generation of LLMs. The IMOSC currently contains a carefully curated collection of the easiest possible problems from difficult competitions, such as the International Mathematical Olympiad (IMO). Problem hardness is measured by applying a mixture of (objective and subjective) difficulty filters to the original problems. We release the full dataset under the link below to encourage transparent evaluation of LLMs and theorem provers toward their mathematical proof-generating abilities: www.imo-small-challenge.io
en
dc.language.iso
en
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dc.subject
IMO Small Challenge
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dc.title
The IMO Small Challenge: Not-Too-Hard Olympiad Math Datasets for LLMs
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dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.contributor.affiliation
University of Oxford, United Kingdom of Great Britain and Northern Ireland (the)
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dc.contributor.affiliation
University of Cambridge, United Kingdom of Great Britain and Northern Ireland (the)
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dc.contributor.affiliation
California Institute of Technology, United States of America (the)