Comparing Large Language Models and Human Programmers for Generating Programming Code
The performance of seven large language models (LLMs) in generating programming code using various prompt strategies, programming languages, and task difficulties is systematically evaluated. GPT‐4 substantially outperforms other LLMs, including Gemini Ultra and Claude 2. The coding performance of G...
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| Vydané v: | Advanced science Ročník 12; číslo 8; s. e2412279 - n/a |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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Germany
John Wiley & Sons, Inc
01.02.2025
John Wiley and Sons Inc Wiley |
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| ISSN: | 2198-3844, 2198-3844 |
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| Abstract | The performance of seven large language models (LLMs) in generating programming code using various prompt strategies, programming languages, and task difficulties is systematically evaluated. GPT‐4 substantially outperforms other LLMs, including Gemini Ultra and Claude 2. The coding performance of GPT‐4 varies considerably with different prompt strategies. In most LeetCode and GeeksforGeeks coding contests evaluated in this study, GPT‐4, employing the optimal prompt strategy, outperforms 85 percent of human participants in a competitive environment, many of whom are students and professionals with moderate programming experience. GPT‐4 demonstrates strong capabilities in translating code between different programming languages and in learning from past errors. The computational efficiency of the code generated by GPT‐4 is comparable to that of human programmers. GPT‐4 is also capable of handling broader programming tasks, including front‐end design and database operations. These results suggest that GPT‐4 has the potential to serve as a reliable assistant in programming code generation and software development. A programming assistant is designed based on an optimal prompt strategy to facilitate the practical use of LLMs for programming.
The performance of seven large language models is evaluated for programming code generation across various prompts, languages, and task difficulties. GPT‐4 consistently outperforms other models and excels in tasks such as code translation, error learning, and efficient code generation. It surpasses 85% of human participants in most LeetCode and GeeksforGeeks contests when using the optimal prompt strategy. |
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| AbstractList | Abstract The performance of seven large language models (LLMs) in generating programming code using various prompt strategies, programming languages, and task difficulties is systematically evaluated. GPT‐4 substantially outperforms other LLMs, including Gemini Ultra and Claude 2. The coding performance of GPT‐4 varies considerably with different prompt strategies. In most LeetCode and GeeksforGeeks coding contests evaluated in this study, GPT‐4, employing the optimal prompt strategy, outperforms 85 percent of human participants in a competitive environment, many of whom are students and professionals with moderate programming experience. GPT‐4 demonstrates strong capabilities in translating code between different programming languages and in learning from past errors. The computational efficiency of the code generated by GPT‐4 is comparable to that of human programmers. GPT‐4 is also capable of handling broader programming tasks, including front‐end design and database operations. These results suggest that GPT‐4 has the potential to serve as a reliable assistant in programming code generation and software development. A programming assistant is designed based on an optimal prompt strategy to facilitate the practical use of LLMs for programming. The performance of seven large language models (LLMs) in generating programming code using various prompt strategies, programming languages, and task difficulties is systematically evaluated. GPT-4 substantially outperforms other LLMs, including Gemini Ultra and Claude 2. The coding performance of GPT-4 varies considerably with different prompt strategies. In most LeetCode and GeeksforGeeks coding contests evaluated in this study, GPT-4, employing the optimal prompt strategy, outperforms 85 percent of human participants in a competitive environment, many of whom are students and professionals with moderate programming experience. GPT-4 demonstrates strong capabilities in translating code between different programming languages and in learning from past errors. The computational efficiency of the code generated by GPT-4 is comparable to that of human programmers. GPT-4 is also capable of handling broader programming tasks, including front-end design and database operations. These results suggest that GPT-4 has the potential to serve as a reliable assistant in programming code generation and software development. A programming assistant is designed based on an optimal prompt strategy to facilitate the practical use of LLMs for programming.The performance of seven large language models (LLMs) in generating programming code using various prompt strategies, programming languages, and task difficulties is systematically evaluated. GPT-4 substantially outperforms other LLMs, including Gemini Ultra and Claude 2. The coding performance of GPT-4 varies considerably with different prompt strategies. In most LeetCode and GeeksforGeeks coding contests evaluated in this study, GPT-4, employing the optimal prompt strategy, outperforms 85 percent of human participants in a competitive environment, many of whom are students and professionals with moderate programming experience. GPT-4 demonstrates strong capabilities in translating code between different programming languages and in learning from past errors. The computational efficiency of the code generated by GPT-4 is comparable to that of human programmers. GPT-4 is also capable of handling broader programming tasks, including front-end design and database operations. These results suggest that GPT-4 has the potential to serve as a reliable assistant in programming code generation and software development. A programming assistant is designed based on an optimal prompt strategy to facilitate the practical use of LLMs for programming. The performance of seven large language models (LLMs) in generating programming code using various prompt strategies, programming languages, and task difficulties is systematically evaluated. GPT‐4 substantially outperforms other LLMs, including Gemini Ultra and Claude 2. The coding performance of GPT‐4 varies considerably with different prompt strategies. In most LeetCode and GeeksforGeeks coding contests evaluated in this study, GPT‐4, employing the optimal prompt strategy, outperforms 85 percent of human participants in a competitive environment, many of whom are students and professionals with moderate programming experience. GPT‐4 demonstrates strong capabilities in translating code between different programming languages and in learning from past errors. The computational efficiency of the code generated by GPT‐4 is comparable to that of human programmers. GPT‐4 is also capable of handling broader programming tasks, including front‐end design and database operations. These results suggest that GPT‐4 has the potential to serve as a reliable assistant in programming code generation and software development. A programming assistant is designed based on an optimal prompt strategy to facilitate the practical use of LLMs for programming. The performance of seven large language models (LLMs) in generating programming code using various prompt strategies, programming languages, and task difficulties is systematically evaluated. GPT‐4 substantially outperforms other LLMs, including Gemini Ultra and Claude 2. The coding performance of GPT‐4 varies considerably with different prompt strategies. In most LeetCode and GeeksforGeeks coding contests evaluated in this study, GPT‐4, employing the optimal prompt strategy, outperforms 85 percent of human participants in a competitive environment, many of whom are students and professionals with moderate programming experience. GPT‐4 demonstrates strong capabilities in translating code between different programming languages and in learning from past errors. The computational efficiency of the code generated by GPT‐4 is comparable to that of human programmers. GPT‐4 is also capable of handling broader programming tasks, including front‐end design and database operations. These results suggest that GPT‐4 has the potential to serve as a reliable assistant in programming code generation and software development. A programming assistant is designed based on an optimal prompt strategy to facilitate the practical use of LLMs for programming. The performance of seven large language models (LLMs) in generating programming code using various prompt strategies, programming languages, and task difficulties is systematically evaluated. GPT‐4 substantially outperforms other LLMs, including Gemini Ultra and Claude 2. The coding performance of GPT‐4 varies considerably with different prompt strategies. In most LeetCode and GeeksforGeeks coding contests evaluated in this study, GPT‐4, employing the optimal prompt strategy, outperforms 85 percent of human participants in a competitive environment, many of whom are students and professionals with moderate programming experience. GPT‐4 demonstrates strong capabilities in translating code between different programming languages and in learning from past errors. The computational efficiency of the code generated by GPT‐4 is comparable to that of human programmers. GPT‐4 is also capable of handling broader programming tasks, including front‐end design and database operations. These results suggest that GPT‐4 has the potential to serve as a reliable assistant in programming code generation and software development. A programming assistant is designed based on an optimal prompt strategy to facilitate the practical use of LLMs for programming. The performance of seven large language models is evaluated for programming code generation across various prompts, languages, and task difficulties. GPT‐4 consistently outperforms other models and excels in tasks such as code translation, error learning, and efficient code generation. It surpasses 85% of human participants in most LeetCode and GeeksforGeeks contests when using the optimal prompt strategy. The performance of seven large language models (LLMs) in generating programming code using various prompt strategies, programming languages, and task difficulties is systematically evaluated. GPT‐4 substantially outperforms other LLMs, including Gemini Ultra and Claude 2. The coding performance of GPT‐4 varies considerably with different prompt strategies. In most LeetCode and GeeksforGeeks coding contests evaluated in this study, GPT‐4, employing the optimal prompt strategy, outperforms 85 percent of human participants in a competitive environment, many of whom are students and professionals with moderate programming experience. GPT‐4 demonstrates strong capabilities in translating code between different programming languages and in learning from past errors. The computational efficiency of the code generated by GPT‐4 is comparable to that of human programmers. GPT‐4 is also capable of handling broader programming tasks, including front‐end design and database operations. These results suggest that GPT‐4 has the potential to serve as a reliable assistant in programming code generation and software development. A programming assistant is designed based on an optimal prompt strategy to facilitate the practical use of LLMs for programming. The performance of seven large language models is evaluated for programming code generation across various prompts, languages, and task difficulties. GPT‐4 consistently outperforms other models and excels in tasks such as code translation, error learning, and efficient code generation. It surpasses 85% of human participants in most LeetCode and GeeksforGeeks contests when using the optimal prompt strategy. |
| Author | Ji, Zhicheng Hou, Wenpin |
| AuthorAffiliation | 1 Department of Biostatistics Mailman School of Public Health Columbia University New York City NY 10032 USA 2 Department of Biostatistics and Bioinformatics Duke University School of Medicine Durham NC 07024 USA |
| AuthorAffiliation_xml | – name: 2 Department of Biostatistics and Bioinformatics Duke University School of Medicine Durham NC 07024 USA – name: 1 Department of Biostatistics Mailman School of Public Health Columbia University New York City NY 10032 USA |
| Author_xml | – sequence: 1 givenname: Wenpin surname: Hou fullname: Hou, Wenpin email: wh2526@cumc.columbia.edu organization: Columbia University – sequence: 2 givenname: Zhicheng orcidid: 0000-0002-9457-4704 surname: Ji fullname: Ji, Zhicheng email: zhicheng.ji@duke.edu organization: Duke University School of Medicine |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39736107$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1371/journal.pdig.0000198 10.1126/science.abq1158 10.1016/j.diii.2023.02.003 10.1145/3196398.3196408 10.1038/s41592-024-02235-4 10.15302/J-QB-023-0327 10.1145/3587102.3588814 10.1111/j.2517-6161.1995.tb02031.x 10.1145/3568812.3603474 |
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| Copyright | 2024 The Author(s). published by Wiley‐VCH GmbH 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH. 2025. This work is published under http://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Snippet | The performance of seven large language models (LLMs) in generating programming code using various prompt strategies, programming languages, and task... Abstract The performance of seven large language models (LLMs) in generating programming code using various prompt strategies, programming languages, and task... |
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| SubjectTerms | artificial intelligence Computer programming Datasets Design Feedback Humans human‐computer interaction Large Language Models Performance evaluation Programming Languages Python Software Software development |
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| Title | Comparing Large Language Models and Human Programmers for Generating Programming Code |
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| Volume | 12 |
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