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Programming languages can benefit from one another by utilizing a pre-trainedmodel for software engineering tasks such as code summarization and method nameprediction. While full fine-tuning of Code Language Models (Code-LMs) has beenexplored for multilingual knowledge transfer, research on Parameter EfficientFine-Tuning (PEFT) for this purpose is limited. AdapterFusion, a PEFTarchitecture, aims to enhance task performance by leveraging information frommultiple languages but primarily focuses on the target language.  To address this, we propose AdvFusion, a novel PEFT-based approach thateffectively learns from other languages before adapting to the target task.Evaluated on code summarization and method name prediction, AdvFusionoutperforms AdapterFusion by up to 1.7 points and surpasses LoRA with gains of1.99, 1.26, and 2.16 for Ruby, JavaScript, and Go, respectively. We open-sourceour scripts for replication purposes.

AdvFusion: Adapter-based Knowledge Transfer for Code Summarization on Code Language Models