目标:大语言模型驱动的可解释的仇恨言论检测
Muhammad Haseeb Hashir1, Memoona1, Sung Won Kim2
1Information and Communication Engineering, Yeungnam University, Gyeongsan, Gyeongbuk, Republic of South Korea.
PeerJ. Computer science
|June 26, 2025
概括
本研究引入了一种新的方法,用于在线检测仇恨言论,使用大型语言模型 (LLM) 来解释他们的决定. 这种方法提高了内容调节的准确性和透明度.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 计算社会科学 计算社会科学
背景情况:
- 在社交媒体上,用户生成的内容给大规模检测仇恨言论带来了挑战.
- 传统的手动调节是不切实际的;需要自动化解决方案.
- 现有的深度学习模型缺乏透明度,限制了它们在微妙内容调节中的实用性.
研究的目的:
- 使用先进的语言模型开发透明的仇恨言论检测系统.
- 利用大型语言模型 (LLM) 进行可解释的内容调节.
主要方法:
- 使用了Mistral-7B,这是一个最先进的语言模型.
- 开发了一个框架,LLM可以为仇恨言论的检测产生明确的理由.
- 将LLM生成的理性整合到专业分类器中,以进行可解释的调节.
主要成果:
- 纳入LLM生成的解释显著提高了仇恨言论检测的解释性.
- 该方法改善了识别煽动性和歧视性言论的准确性.
- 该系统为每个调节决定提供了明确的分析理由.
结论:
- 通过LLM生成的推理提高了自动检测仇恨言论的透明度和准确性.
- 这种方法解决了在内容调节中对可解释AI的关键需求.
- 该框架为细微而可扩展的在线内容调节提供了一个实用的解决方案.
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