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English(EN) Hate Speech Classification In Roman Urdu: A Comparative Study On Parameter Efficient Fine-Tuning And Prompt Engineering

AI模型在多语言和基于梗图的仇恨言论检测方面存在困难

作者 PulseAugur 编辑部 · [4 个来源] ·

研究人员正在探索先进方法以提高AI检测仇恨言论的能力，特别是在多语言和多模态的背景下。一项研究侧重于训练时可解释性，以使AI推理与人类的理由保持一致，从而提高在英语和Hinglish语中检测反穆斯林仇恨言论的准确性和可解释性。另一篇论文定性分析了LLaVA-7B、Qwen-VL、GPT-4o mini和Claude 3 Haiku等最先进的视觉语言模型在识别梗图中的仇恨言论方面的有效性，超越了简单的准确性评估，以评估它们的理由。第三项研究调查了LLM在乌尔都语仇恨言论检测中的跨脚本安全不一致性，揭示了原始脚本和英文翻译之间显著的标签不稳定性，并指出了当前该语言安全评估中的不足。 AI

影响 多语言和多模态仇恨言论检测的进步可能带来更细致、更具文化意识的内容审核系统。

排序理由 该集群包含多篇在arXiv上发表的学术论文，详细介绍了AI安全和模型在仇恨言论检测方面的能力研究。

在 arXiv cs.AI 阅读 →

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AI模型在多语言和基于梗图的仇恨言论检测方面存在困难

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该集群包含多篇在arXiv上发表的学术论文，详细介绍了AI安全和模型在仇恨言论检测方面的能力研究。
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报道来源 [4]

  1. arXiv cs.AI TIER_1 English(EN) · Muhammad Deedahwar Mazhar Qureshi, Sannaan Khan, Muhammad Atif Qureshi, Wael Rashwan ·

    多语言仇恨言论检测的训练时可解释性：使模型推理与人类解释保持一致

    arXiv:2608.26125v1 Announce Type: cross Abstract: Online hate against Muslim communities often appears in culturally coded, multilingual forms that evade conventional AI moderation. Such systems, though accurate, remain opaque and risk bias, over-censorship, or under-moderation, …

  2. arXiv cs.AI TIER_1 English(EN) · Muhammad Jawad Chowdhury, Adiba Hasan, Ishrak Hossain, Shahriar Ivan, Sabbir Ahmed ·

    超越准确性：对用于检测梗图仇恨言论的视觉语言模型的定性分析

    arXiv:2608.26143v1 Announce Type: cross Abstract: Memes have turned out to be a powerful tool through which individuals share their ideas concerning contemporary social and political problems. Their anonymity, as well as their ability to go viral, make them a powerful medium for …

  3. arXiv cs.AI TIER_1 English(EN) · Fawzia Zehra (Fuzzy), Kara-Isitt, Sonal Khosla, Stephen Swift ·

    'Ghaib in Translation' 即“看不见的伤害”：使用 LLM 仇恨言论检测中的“Missed-in-Urdu”分数衡量跨脚本安全不一致性

    arXiv:2608.24191v1 Announce Type: cross Abstract: Urdu, the world's tenth most spoken language with 246 million speakers, remains almost entirely absent from mainstream LLM safety evaluation and nine years of WOAH proceedings. To investigate whether this absence has measurable co…

  4. arXiv cs.AI TIER_1 English(EN) · Toneema Zubair ·

    罗马乌尔都语中的仇恨言论分类：参数高效微调与提示工程的比较研究

    arXiv:2608.21408v1 Announce Type: new Abstract: Due to the widespread accessibility of the internet and social media, toxic and hateful con-tent has grown exponentially, causing significant distress and negative societal impacts. Ro-man Urdu, a low-resource language used in Pakis…

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