CATCH-ME if you RAG：针对仇恨与虚假信息交流的上下文注释多轮对抗言论数据集
CATCH-ME if you RAG: a dataset of Contextually Annotated multi-Turn Counterspeech against Hate and Misinformation Exchanges
AI总结：
提出首个大规模、专家策划的多语言对话数据集，覆盖仇恨与虚假信息重叠问题，包含事实核查锚定和跨度标注，支持RAG系统训练更可信的对抗言论模型。
摘要：
在线仇恨言论和虚假信息经常重叠，但NLP研究主要将它们孤立处理。虽然LLMs代表了协助人类针对这两种威胁生成对抗言论的可扩展解决方案，但零样本模型经常生成重复和模糊的回应，凸显了需要高质量示例来指导模型生成。然而，现有的针对仇恨和虚假信息重叠的对抗言论数据集很少，且仅限于单轮英语对话，而现实中的交互跨越多个轮次和语言。为弥补这一差距，我们引入了第一个大规模、专家策划的多语言对话数据集，处理仇恨与虚假信息的交叉点。为确保事实基础，对话还锚定在已验证的外部知识（即事实核查文章和非政府组织报告）中，并包含文档级和块级跨度标注，使其可直接应用于RAG系统。该新资源涵盖五种语言，针对七个边缘化群体的仇恨，能够训练和评估更具说服力、基于事实的对抗言论模型。
英文摘要：
Online hate speech and misinformation frequently overlap, yet NLP research has mainly treated them in isolation. While LLMs represent a scalable solution for assisting humans in the generation of counterspeech for both threats, zero-shot models frequently generate repetitive and vague responses, underscoring the need for high-quality examples to steer model generation. However, existing counterspeech datasets against the overlap of hate and misinformation are scarce and limited to single-turn English dialogues, while real-life interactions span across multiple turns and languages. To bridge this gap, we introduce the first large-scale, expert-curated, multilingual dataset of dialogues tackling the intersection of hate and misinformation. To ensure factual grounding, the dialogues are also anchored in verified external knowledge (i.e., fact-checking articles and NGO reports) and include document- and chunk-level span annotations, making it directly applicable for RAG systems. Covering five languages and targeting hate directed at seven marginalized groups, this novel resource enables the training and evaluation of more persuasive, factually grounded counterspeech models.
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。
