Evaluating the Performance of Large Language Models in Competitive Programming: A Multi-Year, Multi-Grade Analysis

• URL: http://arxiv.org/abs/2409.09054v1
• Date: Sat, 31 Aug 2024 10:39:54 GMT
• Title: Evaluating the Performance of Large Language Models in Competitive Programming: A Multi-Year, Multi-Grade Analysis
• Authors: Adrian Marius Dumitran, Adrian Catalin Badea, Stefan-Gabriel Muscalu,
• Abstract summary: This study explores the performance of large language models (LLMs) in solving competitive programming problems from the Romanian Informatics Olympiad at the county level. We collected and analyzed a dataset comprising 304 challenges from 2002 to 2023. The analysis revealed significant variations in LLM performance across different grades and problem types.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This study explores the performance of large language models (LLMs) in solving competitive programming problems from the Romanian Informatics Olympiad at the county level. Romania, a leading nation in computer science competitions, provides an ideal environment for evaluating LLM capabilities due to its rich history and stringent competition standards. We collected and analyzed a dataset comprising 304 challenges from 2002 to 2023, focusing on solutions written by LLMs in C++ and Python for these problems. Our primary goal is to understand why LLMs perform well or poorly on different tasks. We evaluated various models, including closed-source models like GPT-4 and open-weight models such as CodeLlama and RoMistral, using a standardized process involving multiple attempts and feedback rounds. The analysis revealed significant variations in LLM performance across different grades and problem types. Notably, GPT-4 showed strong performance, indicating its potential use as an educational tool for middle school students. We also observed differences in code quality and style across various LLMs

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