标题： 通过CodeWars的Katas评估GPT的编程能力 显示英文标题

标题： Evaluating GPT's Programming Capability through CodeWars' Katas

摘要： 在人工智能（AI）这一快速发展的领域中，了解面向编程的模型的能力和局限性至关重要。 本文对生成式预训练变换器（GPT）模型，特别是GPT-3.5和GPT-4，在不同难度级别的编码问题上的编程能力进行了新颖的评估，这些问题来源于Codewars。 实验结果揭示了在3kyu级别存在一个明显的分界线，超出该级别的问题，这些GPT模型难以提供解决方案。 这些发现促使提出了一种用于编码问题复杂度的衡量方法，该方法结合了问题难度和解决问题所需的时间。 研究强调了AI模型需要具备验证能力和创造性思维能力，以更好地模仿人类的问题解决技术。 未来的工作旨在完善这种提出的复杂度衡量方法，增强AI模型的这些建议能力，并开发一种客观的编程问题难度衡量方法。 这项研究的结果为提高AI编程能力并推进AI问题解决能力的前沿提供了无价的见解。 显示英文摘要

摘要： In the burgeoning field of artificial intelligence (AI), understanding the capabilities and limitations of programming-oriented models is crucial. This paper presents a novel evaluation of the programming proficiency of Generative Pretrained Transformer (GPT) models, specifically GPT-3.5 and GPT-4, against coding problems of varying difficulty levels drawn from Codewars. The experiments reveal a distinct boundary at the 3kyu level, beyond which these GPT models struggle to provide solutions. These findings led to the proposal of a measure for coding problem complexity that incorporates both problem difficulty and the time required for solution. The research emphasizes the need for validation and creative thinking capabilities in AI models to better emulate human problem-solving techniques. Future work aims to refine this proposed complexity measure, enhance AI models with these suggested capabilities, and develop an objective measure for programming problem difficulty. The results of this research offer invaluable insights for improving AI programming capabilities and advancing the frontier of AI problem-solving abilities.