Large language models (LLMs) have brought a paradigm shift to the field of
code generation, offering the potential to enhance the software development
process. However, previous research mainly focuses on the accuracy of code
generation, while coding style differences between LLMs and human developers
remain under-explored. In this paper, we empirically analyze the differences in
coding style between the code generated by mainstream Code LLMs and the code
written by human developers, and summarize coding style inconsistency taxonomy.
Specifically, we first summarize the types of coding style inconsistencies by
manually analyzing a large number of generation results. We then compare the
code generated by Code LLMs with the code written by human programmers in terms
of readability, conciseness, and robustness. The results reveal that LLMs and
developers have different coding styles. Additionally, we study the possible
causes of these inconsistencies and provide some solutions to alleviate the
problem.

通过实证分析编码生成的主要模型（LLMs）和人类开发者的编码风格不一致，总结编码风格的不一致分类法，并对可行性、简洁性和健壮性进行比较，揭示 LLMs 和开发者具有不同的编码风格，并研究这些不一致的可能原因并提供解决方案。

大型语言模型中的编码风格一致性研究

Beyond Functional Correctness: Investigating Coding Style  Inconsistencies in Large Language Models

Large Language Models (LLMs) have demonstrated great potential for assisting
developers in their daily development. However, most research focuses on
generating correct code, how to use LLMs to generate personalized code has
seldom been investigated. To bridge this gap, we proposed MPCoder (Multi-user
Personalized Code Generator) to generate personalized code for multiple users.
To better learn coding style features, we utilize explicit coding style
residual learning to capture the syntax code style standards and implicit style
learning to capture the semantic code style conventions. We train a multi-user
style adapter to better differentiate the implicit feature representations of
different users through contrastive learning, ultimately enabling personalized
code generation for multiple users. We further propose a novel evaluation
metric for estimating similarities between codes of different coding styles.
The experimental results show the effectiveness of our approach for this novel
task.

利用 MPCoder（多用户个性化代码生成器）通过明确的代码风格残差学习和隐式风格学习，训练多用户风格适配器进行对比学习，从而实现多用户个性化代码生成，同时提出了一种用于估计不同代码风格之间相似性的新型评估指标。实验结果证明了我们方法在这一新任务中的有效性。