Utilization of Pre-trained Language Model for Adapter-based Knowledge Transfer in Software Engineering
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866910319278817280 |
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| author | Saberi, Iman Fard, Fatemeh Chen, Fuxiang |
| author_facet | Saberi, Iman Fard, Fatemeh Chen, Fuxiang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_08540 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Utilization of Pre-trained Language Model for Adapter-based Knowledge Transfer in Software Engineering Saberi, Iman Fard, Fatemeh Chen, Fuxiang Software Engineering 68N30 D.2.0; I.2.5 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. |
| title | Utilization of Pre-trained Language Model for Adapter-based Knowledge Transfer in Software Engineering |
| topic | Software Engineering 68N30 D.2.0; I.2.5 |
| url | https://arxiv.org/abs/2307.08540 |