对比Transformer模型生成C++代码的能力，分析架构复杂度的影响。
A Comparative Study on Code Generation with Transformers
- 用Transformer架构结合NLP方法自动生成C++代码。
- 在从基础算术到复杂计算的问题上评估模型表现。
- 适合关注代码生成与模型结构设计的研究者。
在自然语言处理广泛影响的背景下，多项研究致力于用自动化系统替代传统手动编码，实现自主生成解决方案。随着代码生成研究的快速发展，研究重点集中在大型语言模型上，因此有必要对基于Transformer架构的模型在不同复杂度下的性能进行比较与评估。本文提出了一项关于使用Transformer进行代码生成的比较研究，结合Transformer架构与NLP方法，自动为多种问题生成C++源代码。通过对比不同架构复杂度的模型在处理多样化问题集（从基础算术到复杂计算）时的鲁棒性，评估其生成能力。
原文摘要 · Abstract (English)
In an era of widespread influence of Natural Language Processing (NLP), there have been multiple research efforts to supplant traditional manual coding techniques with automated systems capable of generating solutions autonomously. With rapid research for code generation and a sole focus on large language models, there emerges a need to compare and evaluate the performance of transformer architectures based on several complexities of the model. This paper introduces the concept of a "A Comparative Study on Code Generation with Transformers," a model based on Transformer architecture, and NLP methodologies to automatically generate C++ source code for different varieties of problems. Here, a comparative study is performed to evaluate the robustness of transformer-based models on the basis of their architecture complexities and their capability to handle diverse problem sets, from basic arithmetic to complex computations.
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