SubmissionNumber#=%=#289
FinalPaperTitle#=%=#Groningen Team F at SemEval-2024 Task 8: Detecting Machine-Generated Text using Feature-Based Machine Learning Models
ShortPaperTitle#=%=#
NumberOfPages#=%=#7
CopyrightSigned#=%=#Björn
JobTitle#==#
Organization#==#University of Groningen
Broerstraat 5, 9712 CP Groningen
Netherlands
Abstract#==#Large language models (LLMs) have shown remarkable capability of creating fluent responses to a wide variety of user queries. However, this also comes with concerns regarding the spread of misinformation and potential misuse within educational context. In this paper we describe our contribution to SemEval-2024 Task 8 (Wang et al., 2024), a shared task created around detecting machine-generated text. We aim to create several feature-based models that can detect whether a text is machine-generated or human-written. In the end, we obtained an accuracy of 0.74 on the binary human-written vs. machine-generated text classification task (Subtask A monolingual) and an accuracy of 0.61 on the multi-way machine-generated text-classification task (Subtask B). For future work, more features and models could be implemented.
Author{1}{Firstname}#=%=#Rina
Author{1}{Lastname}#=%=#Donker
Author{1}{Email}#=%=#t.r.donker@student.rug.nl
Author{1}{Affiliation}#=%=#RuG
Author{2}{Firstname}#=%=#Björn
Author{2}{Lastname}#=%=#Overbeek
Author{2}{Username}#=%=#bbjoverbeek
Author{2}{Email}#=%=#b.b.j.overbeek@student.rug.nl
Author{2}{Affiliation}#=%=#RuG
Author{3}{Firstname}#=%=#Dennis van
Author{3}{Lastname}#=%=#Thulden
Author{3}{Email}#=%=#d.l.van.thulden@student.rug.nl
Author{3}{Affiliation}#=%=#RuG
Author{4}{Firstname}#=%=#Oscar
Author{4}{Lastname}#=%=#Zwagers
Author{4}{Email}#=%=#o.y.zwagers@student.rug.nl
Author{4}{Affiliation}#=%=#RuG

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