From Struggle (06-2024) to Mastery (02-2025) LLMs Conquer Advanced Algorithm Exams and Pave the Way for Editorial Generation
This work addresses the problem of assessing and enhancing LLMs' problem-solving capabilities in advanced algorithm education for instructors and students, representing an incremental advancement by applying existing methods to new data.
The paper evaluated state-of-the-art Large Language Models on challenging university-level algorithms exams, finding that recent models achieve scores comparable to top-performing students and demonstrate robust reasoning on complex algorithmic tasks, though difficulties persist with graph-based problems.
This paper presents a comprehensive evaluation of the performance of state-of-the-art Large Language Models (LLMs) on challenging university-level algorithms exams. By testing multiple models on both a Romanian exam and its high-quality English translation, we analyze LLMs' problem-solving capabilities, consistency, and multilingual performance. Our empirical study reveals that the most recent models not only achieve scores comparable to top-performing students but also demonstrate robust reasoning skills on complex, multi-step algorithmic challenges, even though difficulties remain with graph-based tasks. Building on these findings, we explore the potential of LLMs to support educational environments through the generation of high-quality editorial content, offering instructors a powerful tool to enhance student feedback. The insights and best practices discussed herein pave the way for further integration of generative AI in advanced algorithm education.