The recent advancements in Transformer-based Language Models have
demonstrated significant potential in enhancing the multilingual capabilities
of these models. The remarkable progress made in this domain not only applies
to natural language tasks but also extends to the domain of programming
languages. Despite the ability of these models to learn from multiple
languages, evaluations typically focus on particular combinations of the same
languages. In this study, we evaluate the similarity of programming languages
by analyzing their representations using a CodeBERT-based model. Our
experiments reveal that token representation in languages such as C++, Python,
and Java exhibit proximity to one another, whereas the same tokens in languages
such as Mathematica and R display significant dissimilarity. Our findings
suggest that this phenomenon can potentially result in performance challenges
when dealing with diverse languages. Thus, we recommend using our similarity
measure to select a diverse set of programming languages when training and
evaluating future models.

在这项研究中，我们通过使用基于 CodeBERT 的模型分析编程语言的表示来评估编程语言的相似性，发现 C++、Python 和 Java 等语言的标记表示彼此接近，而 Mathematica 和 R 等语言的标记表示存在明显的差异。我们的发现表明，这一现象可能在处理多种语言时导致性能挑战，因此我们建议在训练和评估未来的模型时选择多样化的编程语言。

关于编程语言模型训练和评估的语言选择影响

On the Impact of Language Selection for Training and Evaluating  Programming Language Models

The use of packaged libraries can significantly shorten the software
development cycle by improving the quality and readability of code. In this
paper, we present a recommendation engine called Librarian for open source
libraries. A candidate library package is recommended for a given context if:
1) it has been frequently used with the imported libraries in the program; 2)
it has similar functionality to the imported libraries in the program; 3) it
has similar functionality to the developer's implementation, and 4) it can be
used efficiently in the context of the provided code. We apply the
state-of-the-art CodeBERT-based model for analysing the context of the source
code to deliver relevant library recommendations to users.

本文介绍了开源库的推荐引擎 Librarian，使用基于 CodeBERT 的模型分析源代码的上下文，根据程序中导入的库和开发者的实现建议候选库，以达到缩短软件开发周期的目的。