International Journal For Multidisciplinary Research

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Fine-tuning Pre-trained Language Models to Detect In-game Trash Talks

Author(s) Daniel Fesalbon, Arvin De La Cruz, Marvin Mallari, Nelson Rodelas
Country Philippines
Abstract Common problems in playing online mobile and computer games were related to toxic behavior and abusive communication among players. Based on different reports and studies, the study also discusses the impact of online hate speech and toxicity on players' in-game performance and overall well-being. This study investigates the capability of pre-trained language models to classify or detect trash talk or toxic in-game messages. The study employs and evaluates the performance of pre-trained BERT and GPT language models in detecting toxicity within in-game chats. Using publicly available APIs, in-game chat data from DOTA 2 game matches were collected, processed, reviewed, and labeled as non-toxic, mild (toxicity), and toxic. The study was able to collect around two thousand in-game chats to train and test BERT (Base-uncased), BERT (Large-uncased), and GPT-3 models. Based on the three models’ state-of-the-art performance, this study concludes pre-trained language models’ promising potential for addressing online hate speech and in-game insulting trash talk.
Keywords BERT, GPT, In-game Trash Talks, Toxic Chat Detection
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-03-13
Cite This Fine-tuning Pre-trained Language Models to Detect In-game Trash Talks - Daniel Fesalbon, Arvin De La Cruz, Marvin Mallari, Nelson Rodelas - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.14927
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.14927
Short DOI https://doi.org/gtmzsg
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