Programming languages can benefit from one another by utilizing a pre-trained model for software engineering tasks such as code summarization and method name prediction. While full fine-tuning of Code Language Models (Code-LMs) has been explored for multilingual knowledge transfer, research on Parameter Efficient Fine-Tuning (PEFT) for this purpose is limited. AdapterFusion, a PEFT architecture, aims to enhance task performance by leveraging information from multiple languages but primarily focuses on the target language. To address this, we propose AdvFusion, a novel PEFT-based approach that effectively learns from other languages before adapting to the target task. Evaluated on code summarization and method name prediction, AdvFusion outperforms AdapterFusion by up to 1.7 points and surpasses LoRA with gains of 1.99, 1.26, and 2.16 for Ruby, JavaScript, and Go, respectively. We open-source our scripts for replication purposes.

翻译：编程语言可通过利用预训练模型完成代码摘要和方法名预测等软件工程任务而相互受益。尽管已有研究探索通过全参数微调实现代码语言模型的多语言知识迁移，但针对此目的的参数高效微调研究仍较为有限。AdapterFusion作为一种参数高效微调架构，旨在通过整合多语言信息提升任务性能，但其主要关注目标语言。为此，我们提出AdvFusion——一种基于参数高效微调的新方法，该方法在适应目标任务前能有效从其他语言中学习知识。在代码摘要和方法名预测任务上的评估表明，AdvFusion较AdapterFusion提升最高达1.7个点；相较于LoRA方法，在Ruby、JavaScript和Go语言上分别获得1.99、1.26和2.16的性能增益。我们已开源相关脚本以便复现研究结果。