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Comparing Large Language Models and Human Programmers for Generating Programming Code.

Wenpin Hou1, Zhicheng Ji2

  • 1Department of Biostatistics, Mailman School of Public Health, Columbia University, New York City, NY, 10032, USA.

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Summary
This summary is machine-generated.

GPT-4 excels in code generation, outperforming other large language models (LLMs) and 85% of human programmers in contests. It shows strong capabilities in code translation and efficiency, suggesting its potential as a programming assistant.

Keywords:
artificial intelligencecomputer programminghuman‐computer interactionlarge language models

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Area of Science:

  • Computer Science
  • Artificial Intelligence

Background:

  • Large language models (LLMs) are increasingly explored for code generation.
  • Evaluating LLM performance across diverse programming tasks and strategies is crucial.

Purpose of the Study:

  • To systematically evaluate the code generation performance of seven LLMs.
  • To compare LLM performance against human programmers in competitive coding scenarios.
  • To identify optimal prompt strategies for LLM-assisted programming.

Main Methods:

  • Performance evaluation of seven LLMs on programming tasks of varying difficulty.
  • Utilized diverse prompt strategies and programming languages.
  • Benchmarked GPT-4's output against human participants in coding contests.

Main Results:

  • GPT-4 significantly outperformed other evaluated LLMs (Gemini Ultra, Claude 2).
  • GPT-4 with optimal prompts surpassed 85% of human participants in coding contests.
  • GPT-4 demonstrated proficiency in code translation, error correction, and efficiency comparable to humans.
  • GPT-4 handled diverse tasks including front-end development and database operations.

Conclusions:

  • GPT-4 shows exceptional potential as a reliable assistant for code generation and software development.
  • Optimal prompt engineering is key to maximizing LLM performance in programming.
  • LLM-generated code exhibits human-comparable computational efficiency and task versatility.