从仇恨到和:在COVID-19危机时期,利用大型语言模型进行更安全的言论
August F Y Chao1, Chen-Shu Wang2, Bo-Yi Li3
1Department of Computer Science and Information Engineering, National Penghu University of Science and Technology, Taiwan.
Heliyon
|September 2, 2024
概括
这项研究使用机器学习和大型语言模型 (LLM) 来检测和重新表达中文的流行病相关的攻击性言论. 开发的框架有效地减少了仇恨言论,同时保留了原来的意义,促进了更健康的在线话语.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 随着COVID-19大流行,在线攻击性和谤性语言的使用大幅增加.
- 现有的减轻仇恨言论的方法往往缺乏细微的理解和有效的重新表达能力.
研究的目的:
- 开发和评估一种机器学习框架,用于检测和转换滥用和仇恨言论,特别是在大流行期间的中文语言环境中.
- 加强对攻击性内容的表述,以减少敌意,同时保留核心含义.
主要方法:
- 编制了一套专门的数据集,对中国的侮辱性和仇恨言论进行分析,其中包括30个与流行病相关的术语.
- 使用大型语言模型 (LLM) 实现双层检测模型.
- 整合生成性AI (LLMs) 与隐性迪里克莱特分配 (LDA) 话题建模,以增强转述和语义分析.
主要成果:
- 双层检测模型实现了高准确率:第一阶段94.42%,第二阶段81.48%.
- 综合生成AI和LDA方法成功地重新表达了敌对的陈述,减少了攻击性.
- 重新表述的内容有效地保留了原始陈述的基本主题和含义.
结论:
- 开发的框架显示了通过准确的检测和有效的语义保护转换来缓解在线仇恨言论的巨大潜力.
- 这项研究为打击有害语言的传播和促进更积极的公共话语提供了有价值的工具.
- 该研究强调了将LLM与主题建模结合用于细微语言修改任务的有效性.
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