Publication details

KInIT at SemEval-2024 Task 8: Fine-tuned LLMs for Multilingual Machine-Generated Text Detection

Authors

SPIEGEL Michal DOMINIK Macko

Year of publication 2024
Type Article in Proceedings
Conference Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
MU Faculty or unit

Faculty of Informatics

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.
Related projects:

You are running an old browser version. We recommend updating your browser to its latest version.

More info

By clicking “Accept Cookies”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. Cookie Settings

Necessary Only Accept Cookies