U-MATH: A University-Level Benchmark for Evaluating Mathematical Skills
  in LLMs
Authors: name: Konstantin Chernyshev, name: Vitaliy Polshkov, name: Ekaterina Artemova, name: Alex Myasnikov, name: Vlad Stepanov, name: Alexei Miasnikov, name: Sergei Tilga

The current evaluation of mathematical skills in LLMs is limited, as existing
benchmarks are either relatively small, primarily focus on elementary and
high-school problems, or lack diversity in topics. Additionally, the inclusion
of visual elements in tasks remains largely under-explored.
  To address these gaps, we introduce U-MATH, a novel benchmark of 1,100
unpublished open-ended university-level problems sourced from teaching
materials. It is balanced across six core subjects, with 20% of multimodal
problems. Given the open-ended nature of U-MATH problems, we employ an LLM to
judge the correctness of generated solutions. To this end, we release
mu-MATH, a dataset to evaluate the LLMs' capabilities in judging solutions.
  The evaluation of general domain, math-specific, and multimodal LLMs
highlights the challenges presented by U-MATH. Our findings reveal that LLMs
achieve a maximum accuracy of only 63% on text-based tasks, with even lower 45%
on visual problems. The solution assessment proves challenging for LLMs, with
the best LLM judge having an F1-score of 80% on mu-MATH.