游戏聊天中的高效毒性检测：Embeddings、微调 Transformer 与 LLM 的比较研究
Efficient Toxicity Detection in Gaming Chats: A Comparative Study of Embeddings, Fine-Tuned Transformers and LLMs
AI总结：
本文比较传统机器学习、LLM、微调 transformer 与 RAG 在游戏聊天毒性检测中的准确率、速度和成本，并提出混合持续学习审核架构，证明微调 DistilBERT 具有最优准确率—成本权衡。
摘要：
本文对自然语言处理（NLP）方法在在线游戏聊天中自动检测毒性的表现进行了全面比较分析。研究评估了结合 embeddings 的传统机器学习模型、采用零样本和少样本提示的大型语言模型（LLM）、微调 transformer 模型，以及检索增强生成（RAG）方法。评估框架考察三个关键维度：分类准确率、处理速度和计算成本。文中提出了一种混合审核系统架构，通过自动检测优化人工审核员的工作量，并纳入持续学习机制。实验结果表明，不同方法之间存在显著性能差异，其中微调 DistilBERT 实现了最佳的准确率—成本权衡。这些发现为在动态在线游戏环境中部署具有成本效益且高效的内容审核系统提供了实证依据。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
