Comparing large language models and human programmers for generating programming code
March 01, 2024 Β· Declared Dead Β· π Advancement of science
"No code URL or promise found in abstract"
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Authors
Wenpin Hou, Zhicheng Ji
arXiv ID
2403.00894
Category
cs.SE: Software Engineering
Cross-listed
cs.AI,
cs.CL,
cs.PL
Citations
37
Venue
Advancement of science
Last Checked
4 months ago
Abstract
We systematically evaluated the performance of seven large language models in generating programming code using various prompt strategies, programming languages, and task difficulties. GPT-4 substantially outperforms other large language models, 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. Additionally, 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. These results suggest that GPT-4 has the potential to serve as a reliable assistant in programming code generation and software development.
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