Performance Review on LLM for solving leetcode problems

• URL: http://arxiv.org/abs/2502.15770v2
• Date: Mon, 03 Mar 2025 00:24:08 GMT
• Title: Performance Review on LLM for solving leetcode problems
• Authors: Lun Wang, Chuanqi Shi, Shaoshui Du, Yiyi Tao, Yixian Shen, Hang Zheng, Yanxin Shen, Xinyu Qiu,
• Abstract summary: This paper presents a comprehensive performance evaluation of Large Language Models (LLMs) in solving programming challenges from Leetcode.<n>We generated solutions with multiple LLMs, including GPT-4 and GPT-3.5-turbo.<n>Our results highlight the strengths and limitations of current LLMs in code generation and problem-solving tasks.
• Score: 7.377558533352298
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a comprehensive performance evaluation of Large Language Models (LLMs) in solving programming challenges from Leetcode, a widely used platform for algorithm practice and technical interviews. We began by crawling the Leetcode website to collect a diverse set of problems encompassing various difficulty levels and topics. Using this dataset, we generated solutions with multiple LLMs, including GPT-4 and GPT-3.5-turbo (ChatGPT-turbo). The generated solutions were systematically evaluated for correctness and efficiency. We employed the pass@k metric to assess the success rates within a given number of attempts and analyzed the runtime performance of the solutions. Our results highlight the strengths and limitations of current LLMs [10] in code generation and problem-solving tasks, providing insights into their potential applications and areas for improvement in automated programming assistance.

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