SubmissionNumber#=%=#87
FinalPaperTitle#=%=#KInIT at SemEval-2024 Task 8: Fine-tuned LLMs for Multilingual Machine-Generated Text Detection
ShortPaperTitle#=%=#
NumberOfPages#=%=#7
CopyrightSigned#=%=#Dominik Macko
JobTitle#==#
Organization#==#
Abstract#==#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.
Author{1}{Firstname}#=%=#Michal
Author{1}{Lastname}#=%=#Spiegel
Author{1}{Username}#=%=#kinit-michalspiegel
Author{1}{Email}#=%=#michal.spiegel@intern.kinit.sk
Author{1}{Affiliation}#=%=#Kempelen Institute of Intelligent Technologies
Author{2}{Firstname}#=%=#Dominik
Author{2}{Lastname}#=%=#Macko
Author{2}{Username}#=%=#dominik.macko
Author{2}{Email}#=%=#dominik.macko@kinit.sk
Author{2}{Affiliation}#=%=#Kempelen Institute of Intelligent Technologies

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