Evaluating Large Language Models on Computer Science University Exams in Data Structures

Edan Gabay; Yael Maoz; Jonathan Stahl; Naama Maoz; Abdo Amer; Orr Eilat; Hanoch Levy; Michal Kleinbort; Amir Rubinstein; Adi Haviv

arXiv · 2604.23347

Evaluating Large Language Models on Computer Science University Exams in Data Structures

Edan Gabay·Yael Maoz·Jonathan Stahl·Naama Maoz·Abdo Amer·Orr Eilat·Hanoch Levy·Michal Kleinbort·Amir Rubinstein·Adi Haviv

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Abstract

We present a comprehensive evaluation of Large Language Models (LLMs) on Computer Science (CS) Data Structure examination questions. Our work introduces a new benchmark dataset comprising exam questions from Tel Aviv University (TAU), curated to assess LLMs' abilities in handling closed and multiple-choice questions. We evaluated the performance of OpenAI's GPT 4o and Anthropic's Claude 3.5, popular LLMs, alongside two smaller LLMs, Mathstral 7B and LLaMA 3 8B, across the TAU exams benchmark. Our findings provide insight into the current capabilities of LLMs in CS education.

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Edan Gabay, Yael Maoz, Jonathan Stahl, Naama Maoz, Abdo Amer, Orr Eilat, Hanoch Levy, Michal Kleinbort, Amir Rubinstein, Adi Haviv. 2026-04-25. Evaluating Large Language Models on Computer Science University Exams in Data Structures. https://arxiv.org/abs/2604.23347

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