Efficient Toxicity Detection in Gaming Chats: A Comparative Study of Embeddings, Fine-Tuned Transformers and LLMs

• URL: http://arxiv.org/abs/2510.17924v1
• Date: Mon, 20 Oct 2025 08:03:28 GMT
• Title: Efficient Toxicity Detection in Gaming Chats: A Comparative Study of Embeddings, Fine-Tuned Transformers and LLMs
• Authors: Yehor Tereshchenko, Mika Hämäläinen,
• Abstract summary: This paper presents a comparative analysis of Natural Language Processing (NLP) methods for automated toxicity detection in online gaming chats.<n>Traditional machine learning models with embeddings, large language models (LLMs), with zero-shot and few-shot prompting, fine-tuned transformer models, and retrieval-augmented generation approaches are evaluated.<n>The findings provide empirical evidence for deploying cost-effective, efficient content moderation systems in dynamic online gaming environments.
• Score: 0.18907108368038214
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a comprehensive comparative analysis of Natural Language Processing (NLP) methods for automated toxicity detection in online gaming chats. Traditional machine learning models with embeddings, large language models (LLMs) with zero-shot and few-shot prompting, fine-tuned transformer models, and retrieval-augmented generation (RAG) approaches are evaluated. The evaluation framework assesses three critical dimensions: classification accuracy, processing speed, and computational costs. A hybrid moderation system architecture is proposed that optimizes human moderator workload through automated detection and incorporates continuous learning mechanisms. The experimental results demonstrate significant performance variations across methods, with fine-tuned DistilBERT achieving optimal accuracy-cost trade-offs. The findings provide empirical evidence for deploying cost-effective, efficient content moderation systems in dynamic online gaming environments.

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