Effectiveness of ChatGPT in Coding: A Comparative Analysis of Popular Large Language Models

Author:

Coello Carlos Eduardo Andino1,Alimam Mohammed Nazeh1,Kouatly Rand1ORCID

Affiliation:

1. Faculty of Tech and Software Engineering, University of Europe of Applied Sciences, 14469 Potsdam, Germany

Abstract

This study explores the effectiveness and efficiency of the popular OpenAI model ChatGPT, powered by GPT-3.5 and GPT-4, in programming tasks to understand its impact on programming and potentially software development. To measure the performance of these models, a quantitative approach was employed using the Mostly Basic Python Problems (MBPP) dataset. In addition to the direct assessment of GPT-3.5 and GPT-4, a comparative analysis involving other popular large language models in the AI landscape, notably Google’s Bard and Anthropic’s Claude, was conducted to measure and compare their proficiency in the same tasks. The results highlight the strengths of ChatGPT models in programming tasks, offering valuable insights for the AI community, specifically for developers and researchers. As the popularity of artificial intelligence increases, this study serves as an early look into the field of AI-assisted programming.

Publisher

MDPI AG

Reference31 articles.

1. Language models are few-shot learners;Brown;Adv. Neural Inf. Process. Syst.,2020

2. Fan, L., Li, L., Ma, Z., Lee, S., Yu, H., and Hemphill, L. (2023). A bibliometric review of large language models research from 2017 to 2023. arXiv.

3. Ni, A., Iyer, S., Radev, D., Stoyanov, V., Yih, W.T., Wang, S., and Lin, X.V. (2023, January 23–29). Lever: Learning to verify language-to-code generation with execution. Proceedings of the International Conference on Machine Learning 2023, Honolulu, HI, USA.

4. OpenAI, and Pilipiszyn, A. (2023, November 26). GPT-3 Powers the Next Generation of Apps. Available online: https://openai.com/blog/gpt-3-apps/.

5. Hardesty, L. (2023, July 25). Explained: Neural Networks, MIT News. Massachusetts Institute of Technology. Available online: https://news.mit.edu/2017/explained-neural-networks-deep-learning-0414.

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3