# Utilization of Pre-trained Language Model for Adapter-based Knowledge Transfer in Software Engineering

arXiv: https://arxiv.org/abs/2307.08540 · HTML: https://mlsanity.com/paper/2307.08540/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2023.

## Built on by (most cited first, 3 of 3)

- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models](https://mlsanity.com/paper/2308.10462/) (2023)
- [Automatic Detection of LLM-Generated Code: A Comparative Case Study of Contemporary Models Across Function and Class Granularities](https://mlsanity.com/paper/2409.01382/) (2024)

## Abstract (arXiv)

Software Engineering (SE) Pre-trained Language Models (PLMs), such as CodeBERT, are pre-trained on large code corpora, and their learned knowledge has shown success in transferring into downstream tasks (e.g., code clone detection) through the fine-tuning of PLMs. In Natural Language Processing (NLP), an alternative in transferring the knowledge of PLMs is explored through the use of adapter, a compact and parameter efficient module that is inserted into a PLM. Although the use of adapters has shown promising results in many NLP-based downstream tasks, their application and exploration in SE-based downstream tasks are limited. Here, we study the knowledge transfer using adapters on multiple down-stream tasks including cloze test, code clone detection, and code summarization. These adapters are trained on code corpora and are inserted into a PLM that is pre-trained on English corpora or code corpora. We called these PLMs as NL-PLM and C-PLM, respectively. We observed an improvement in results using NL-PLM over a PLM that does not have adapters, and this suggested that adapters can transfer and utilize useful knowledge from NL-PLM to SE tasks. The results are sometimes on par with or exceed the results of C-PLM; while being more efficient in terms of the number of parameters and training time. Interestingly, adapters inserted into a C-PLM generally yield better results than a traditional fine-tuned C-PLM. Our results open new directions to build more compact models for SE tasks.