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

arXiv: https://arxiv.org/abs/2510.17924 · HTML: https://mlsanity.com/paper/2510.17924/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 42 references that are in the map.
- First posted on arXiv 20 Oct 2025.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2405.06211/) (2024)
- [Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment](https://mlsanity.com/paper/2312.12148/) (2023)
- [Linear Algebraic Structure of Word Senses, with Applications to Polysemy](https://mlsanity.com/paper/1601.03764/) (2016)
- [Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models](https://mlsanity.com/paper/2304.03738/) (2023)
- [word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method](https://mlsanity.com/paper/1402.3722/) (2014)
- [Deceiving Google's Perspective API Built for Detecting Toxic Comments](https://mlsanity.com/paper/1702.08138/) (2017)
- [Adapting Large Language Models for Content Moderation: Pitfalls in Data Engineering and Supervised Fine-tuning](https://mlsanity.com/paper/2310.03400/) (2023)
- [Fine Tuning LLM for Enterprise: Practical Guidelines and Recommendations](https://mlsanity.com/paper/2404.10779/) (2024)
- [Sarcasm Analysis using Conversation Context](https://mlsanity.com/paper/1808.07531/) (2018)
- [Semantically Informed Slang Interpretation](https://mlsanity.com/paper/2205.00616/) (2022)
- [Multilingual Persuasion Detection: Video Games as an Invaluable Data Source for NLP](https://mlsanity.com/paper/2207.04453/) (2022)
- [Addressing interpersonal harm in online gaming communities: the opportunities and challenges for a restorative justice approach](https://mlsanity.com/paper/2211.01524/) (2022)
- [A Comparative Analysis of Ethical and Safety Gaps in LLMs using Relative Danger Coefficient](https://mlsanity.com/paper/2505.04654/) (2025)
- [Linking GloVe with word2vec](https://mlsanity.com/paper/1411.5595/) (2014)
- [Emotion Conditioned Creative Dialog Generation](https://mlsanity.com/paper/2212.02907/) (2022)
- [Analyzing Pok'emon and Mario Streamers' Twitch Chat with LLM-based User Embeddings](https://mlsanity.com/paper/2411.10934/) (2024)

## Built on by (most cited first, 1 of 1)

- [Evaluating OpenAI GPT Models for Translation of Endangered Uralic Languages: A Comparison of Reasoning and Non-Reasoning Architectures](https://mlsanity.com/paper/2512.16287/) (2025)

## Abstract (arXiv)

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.