评估大模型生成反仇恨言论的四种表现，发现内容冗长且难懂。
Think Like a Person Before Responding: A Multi-Faceted Evaluation of Persona-Guided LLMs for Countering Hate
- 从角色设定、可读性、情感基调和伦理安全四方面评估反仇恨内容
- 生成内容普遍冗长，适合大学水平读者，影响传播效果
- 情感引导提升共情但安全性和有效性仍存隐患，适合研究者参考
自动反叙事（CN）是缓解网络仇恨言论的有前景策略，但其情感基调、可及性和伦理风险仍受关注。本文提出一个涵盖四种维度的评估框架：角色设定、语言冗余与可读性、情感基调、伦理鲁棒性。使用 GPT-4o-Mini、Cohere CommandR-7B 及 Meta LLaMA 3.1-70B 模型，在 MT-Conan 与 HatEval 数据集上测试三种提示策略。结果表明，生成的反叙事普遍冗长，适配大学水平读者，限制了传播可达性；情感引导提示虽提升共情与可读性，但在安全性与实际效果方面仍存在隐患。
原文摘要 · Abstract (English)
Automated counter-narratives (CN) offer a promising strategy for mitigating online hate speech, yet concerns about their affective tone, accessibility, and ethical risks remain. We propose a framework for evaluating Large Language Model (LLM)-generated CNs across four dimensions: persona framing, verbosity and readability, affective tone, and ethical robustness. Using GPT-4o-Mini, Cohere's CommandR-7B, and Meta's LLaMA 3.1-70B, we assess three prompting strategies on the MT-Conan and HatEval datasets. Our findings reveal that LLM-generated CNs are often verbose and adapted for people with college-level literacy, limiting their accessibility. While emotionally guided prompts yield more empathetic and readable responses, there remain concerns surrounding safety and effectiveness.
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