Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.17964 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913366804529152 |
|---|---|
| author | Marchitan, Teodor-George Creanga, Claudiu Dinu, Liviu P. |
| author_facet | Marchitan, Teodor-George Creanga, Claudiu Dinu, Liviu P. |
| contents | This paper describes the approach of the UniBuc - NLP team in tackling the SemEval 2024 Task 8: Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection. We explored transformer-based and hybrid deep learning architectures. For subtask B, our transformer-based model achieved a strong \textbf{second-place} out of $77$ teams with an accuracy of \textbf{86.95\%}, demonstrating the architecture's suitability for this task. However, our models showed overfitting in subtask A which could potentially be fixed with less fine-tunning and increasing maximum sequence length. For subtask C (token-level classification), our hybrid model overfit during training, hindering its ability to detect transitions between human and machine-generated text. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17964 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Transformer and Hybrid Deep Learning Based Models for Machine-Generated Text Detection Marchitan, Teodor-George Creanga, Claudiu Dinu, Liviu P. Computation and Language This paper describes the approach of the UniBuc - NLP team in tackling the SemEval 2024 Task 8: Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection. We explored transformer-based and hybrid deep learning architectures. For subtask B, our transformer-based model achieved a strong \textbf{second-place} out of $77$ teams with an accuracy of \textbf{86.95\%}, demonstrating the architecture's suitability for this task. However, our models showed overfitting in subtask A which could potentially be fixed with less fine-tunning and increasing maximum sequence length. For subtask C (token-level classification), our hybrid model overfit during training, hindering its ability to detect transitions between human and machine-generated text. |
| title | Transformer and Hybrid Deep Learning Based Models for Machine-Generated Text Detection |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2405.17964 |