  Recently, Large Langauge Models (LLMs) have gained a lot of attention in the
Software Engineering (SE) community. LLMs or their variants pre-trained on code
are used for many SE tasks. A main approach for adapting LLMs to the downstream
task is to fine-tune the models. However, with having billions-parameters-LLMs,
fine-tuning the models is not practical. An alternative approach is using
Parameter Efficient Fine Tuning (PEFT), in which the model parameters are
frozen and only a few added parameters are trained. Though the LLMs are used
for programming languages such as Python and Java widely, their capability for
low-resource languages is limited. In this work, we empirically study PEFT
methods, LoRA and Compacter, on CodeT5 and CodeLlama. We will assess their
performance compared to fully fine-tuned models, whether they can be used for
knowledge transfer from natural language models to code (using T5 and Llama
models), and their ability to adapt the learned knowledge to an unseen
language. For the unseen language, we aim to study R, as it has a wide
community. The adaptability with less computational costs makes LLMs accessible
in scenarios where heavy computational resources are not available. Moreover,
studying R opens new opportunities for using LLMs for other languages. We
anticipate our findings to showcase the capabilities of PEFT for code LLMs for
R and reveal the improvement areas.
