KInIT at SemEval-2024 Task 8: Fine-tuned LLMs for Multilingual Machine-Generated Text Detection
| Authors | |
|---|---|
| Year of publication | 2024 |
| Type | Article in Proceedings |
| Conference | Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024) |
| MU Faculty or unit | |
| Citation | |
| Web | https://aclanthology.org/2024.semeval-1.84/ |
| Doi | http://dx.doi.org/10.18653/v1/2024.semeval-1.84 |
| Keywords | machine-generated text detection; natural language processing; large language models; ensemble |
| Description | SemEval-2024 Task 8 is focused on multigenerator, multidomain, and multilingual black-box machine-generated text detection. Such a detection is important for preventing a potential misuse of large language models (LLMs), the newest of which are very capable in generating multilingual human-like texts. We have coped with this task in multiple ways, utilizing language identification and parameter-efficient fine-tuning of smaller LLMs for text classification. We have further used the per-language classification-threshold calibration to uniquely combine fine-tuned models predictions with statistical detection metrics to improve generalization of the system detection performance. Our submitted method achieved competitive results, ranking at the fourth place, just under 1 percentage point behind the winner. |
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