MasonTigers at SemEval-2024 Task 8: Performance Analysis of Transformer-based Models on Machine-Generated Text Detection

Fuente: arXiv
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Hauptverfasser: Puspo, Sadiya Sayara Chowdhury, Raihan, Md Nishat, Goswami, Dhiman, Emran, Al Nahian Bin, Ganguly, Amrita, Uzuner, Ozlem
Format: Preprint
Veröffentlicht: 2024
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author Puspo, Sadiya Sayara Chowdhury
Raihan, Md Nishat
Goswami, Dhiman
Emran, Al Nahian Bin
Ganguly, Amrita
Uzuner, Ozlem
author_facet Puspo, Sadiya Sayara Chowdhury
Raihan, Md Nishat
Goswami, Dhiman
Emran, Al Nahian Bin
Ganguly, Amrita
Uzuner, Ozlem
contents This paper presents the MasonTigers entry to the SemEval-2024 Task 8 - Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection. The task encompasses Binary Human-Written vs. Machine-Generated Text Classification (Track A), Multi-Way Machine-Generated Text Classification (Track B), and Human-Machine Mixed Text Detection (Track C). Our best performing approaches utilize mainly the ensemble of discriminator transformer models along with sentence transformer and statistical machine learning approaches in specific cases. Moreover, zero-shot prompting and fine-tuning of FLAN-T5 are used for Track A and B.
format Preprint
id arxiv_https___arxiv_org_abs_2403_14989
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle MasonTigers at SemEval-2024 Task 8: Performance Analysis of Transformer-based Models on Machine-Generated Text Detection
Puspo, Sadiya Sayara Chowdhury
Raihan, Md Nishat
Goswami, Dhiman
Emran, Al Nahian Bin
Ganguly, Amrita
Uzuner, Ozlem
Computation and Language
This paper presents the MasonTigers entry to the SemEval-2024 Task 8 - Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection. The task encompasses Binary Human-Written vs. Machine-Generated Text Classification (Track A), Multi-Way Machine-Generated Text Classification (Track B), and Human-Machine Mixed Text Detection (Track C). Our best performing approaches utilize mainly the ensemble of discriminator transformer models along with sentence transformer and statistical machine learning approaches in specific cases. Moreover, zero-shot prompting and fine-tuning of FLAN-T5 are used for Track A and B.
title MasonTigers at SemEval-2024 Task 8: Performance Analysis of Transformer-based Models on Machine-Generated Text Detection
topic Computation and Language
url https://arxiv.org/abs/2403.14989