Self-Collaboration Code Generation via ChatGPT

Peking University

Indexed incrossref

Abstract

Although large language models (LLMs) have demonstrated remarkable code-generation ability, they still struggle with complex tasks. In real-world software development, humans usually tackle complex tasks through collaborative teamwork, a strategy that significantly controls development complexity and enhances software quality. Inspired by this, we present a self-collaboration framework for code generation employing LLMs, exemplified by ChatGPT. Specifically, through role instructions, (1) Multiple LLM agents act as distinct “experts,” each responsible for a specific subtask within a complex task; (2) Specify the way to collaborate and interact, so that different roles form a virtual team to facilitate each…

Citation impact

175
total citations
FWCI
53.42
Percentile
100%
References
23
Citations per year

Authors

4

Topics & keywords

Keywords
  • Computer science
  • Code generation
  • Code (set theory)
  • Software engineering
  • Programming language
  • Computer security
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