对比多种AI方法，找出游戏聊天中高效检测毒性的最佳方案。
Efficient Toxicity Detection in Gaming Chats: A Comparative Study of Embeddings, Fine-Tuned Transformers and LLMs
- 比较嵌入、微调模型、大模型和检索增强生成的效果
- 微调的DistilBERT在准确率与成本间表现最优
- 适合需要快速响应的在线游戏内容审核场景
本文对自然语言处理技术在在线游戏聊天中的毒性检测应用进行了全面比较分析。评估了传统机器学习模型（基于嵌入）、零样本/少样本提示的大语言模型（LLMs）、微调的Transformer模型以及检索增强生成（RAG）方法。评价框架涵盖分类准确率、处理速度与计算成本三个维度。提出一种混合监管系统架构，通过自动化检测降低人工审核负担，并引入持续学习机制。实验结果表明各方法性能差异显著，微调后的DistilBERT在准确率与成本权衡上表现最佳。研究为动态在线游戏环境部署低成本、高效率的内容审核系统提供了实证依据。
原文摘要 · Abstract (English)
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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