  With the rapid development of Large Language Models (LLMs), it is crucial to
have benchmarks which can evaluate the ability of LLMs on different domains.
One common use of LLMs is performing tasks on scientific topics, such as
writing algorithms, querying databases or giving mathematical proofs. Inspired
by the way university students are evaluated on such tasks, in this paper, we
propose SciEx - a benchmark consisting of university computer science exam
questions, to evaluate LLMs ability on solving scientific tasks. SciEx is (1)
multilingual, containing both English and German exams, and (2) multi-modal,
containing questions that involve images, and (3) contains various types of
freeform questions with different difficulty levels, due to the nature of
university exams. We evaluate the performance of various state-of-the-art LLMs
on our new benchmark. Since SciEx questions are freeform, it is not
straightforward to evaluate LLM performance. Therefore, we provide human expert
grading of the LLM outputs on SciEx. We show that the free-form exams in SciEx
remain challenging for the current LLMs, where the best LLM only achieves
59.4\% exam grade on average. We also provide detailed comparisons between LLM
performance and student performance on SciEx. To enable future evaluation of
new LLMs, we propose using LLM-as-a-judge to grade the LLM answers on SciEx.
Our experiments show that, although they do not perform perfectly on solving
the exams, LLMs are decent as graders, achieving 0.948 Pearson correlation with
expert grading.
