Can LLMs Master Math? Investigating Large Language Models on Math Stack
  Exchange
Ankit Satpute Noah Giessing Andre Greiner-Petter Moritz Schubotz Olaf Teschke Akiko Aizawa Bela Gipp
Large Language Models (LLMs) have demonstrated exceptional capabilities in
various natural language tasks, often achieving performances that surpass those
of humans. Despite these advancements, the domain of mathematics presents a
distinctive challenge, primarily due to its specialized structure and the
precision it demands. In this study, we adopted a two-step approach for
investigating the proficiency of LLMs in answering mathematical questions.
First, we employ the most effective LLMs, as identified by their performance on
math question-answer benchmarks, to generate answers to 78 questions from the
Math Stack Exchange (MSE). Second, a case analysis is conducted on the LLM that
showed the highest performance, focusing on the quality and accuracy of its
answers through manual evaluation. We found that GPT-4 performs best (nDCG of
0.48 and P@10 of 0.37) amongst existing LLMs fine-tuned for answering
mathematics questions and outperforms the current best approach on ArqMATH3
Task1, considering P@10. Our Case analysis indicates that while the GPT-4 can
generate relevant responses in certain instances, it does not consistently
answer all questions accurately. This paper explores the current limitations of
LLMs in navigating complex mathematical problem-solving. Through case analysis,
we shed light on the gaps in LLM capabilities within mathematics, thereby
setting the stage for future research and advancements in AI-driven
mathematical reasoning. We make our code and findings publicly available for
research: \url{https://github.com/gipplab/LLM-Investig-MathStackExchange}