SubmissionNumber#=%=#138
FinalPaperTitle#=%=#Fine-tuning Language Models for AI vs Human Generated Text detection
ShortPaperTitle#=%=#
NumberOfPages#=%=#4
CopyrightSigned#=%=#Sankalp Bahad
JobTitle#==#
Organization#==#
Abstract#==#In this paper, we introduce a machine-
generated text detection system designed to
tackle the challenges posed by the prolifera-
tion of large language models (LLMs). With
the rise of LLMs such as ChatGPT and GPT-4,
there is a growing concern regarding the po-
tential misuse of machine-generated content,
including misinformation dissemination. Our
system addresses this issue by automating the
identification of machine-generated text across
multiple subtasks: binary human-written vs.
machine-generated text classification, multi-
way machine-generated text classification, and
human-machine mixed text detection. We em-
ploy the RoBERTa Base model and fine-tune
it on a diverse dataset encompassing various
domains, languages, and sources. Through
rigorous evaluation, we demonstrate the effec-
tiveness of our system in accurately detecting
machine-generated text, contributing to efforts
aimed at mitigating its potential misuse.
Author{1}{Firstname}#=%=#Sankalp Sanjay
Author{1}{Lastname}#=%=#Bahad
Author{1}{Username}#=%=#sankalp_bahad
Author{1}{Email}#=%=#sankalp.bahad@research.iiit.ac.in
Author{1}{Affiliation}#=%=#IIIT Hyderabad
Author{2}{Firstname}#=%=#Yash
Author{2}{Lastname}#=%=#Bhaskar
Author{2}{Email}#=%=#yash.bhaskar@research.iiit.ac.in
Author{2}{Affiliation}#=%=#IIIT Hyderabad
Author{3}{Firstname}#=%=#Parameswari
Author{3}{Lastname}#=%=#Krishnamurthy
Author{3}{Username}#=%=#parameswari
Author{3}{Email}#=%=#parameshkrishnaa@gmail.com
Author{3}{Affiliation}#=%=#Assistant Professor, IIIT Hyderabad

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