根据读者健康素养水平生成适配的反虚假信息内容
Speaking at the Right Level: Literacy-Controlled Counterspeech Generation with RAG-RL
- 用检索增强生成结合强化学习，按受众理解力定制回复
- 生成内容在可读性和用户偏好上均优于基线模型
- 适合公共卫生传播、社交媒体治理等场景使用
在线健康谣言传播对公共健康构成重大威胁。研究人员探索了自动生成反谣言内容以缓解这一问题的方法。现有方法常生成统一回应，忽略了受众健康素养水平会影响反谣言内容的可及性与效果。本文提出一种可控健康素养框架，采用检索增强生成（RAG）与强化学习（RL）结合的方式，生成适配不同健康素养水平的反谣言内容。具体地，通过检索与特定健康素养水平匹配的知识，确保生成内容既易懂又准确。设计包含主观用户偏好和客观可读性评分的奖励函数，优化生成内容对目标健康素养水平的适配性。实验结果表明，该框架生成的内容在可读性和用户偏好上均优于基线模型。本研究有助于提升反谣言传播的公平性与影响力，增强公众对健康信息的理解与接受度。
原文摘要 · Abstract (English)
Health misinformation spreading online poses a significant threat to public health. Researchers have explored methods for automatically generating counterspeech to health misinformation as a mitigation strategy. Existing approaches often produce uniform responses, ignoring that the health literacy level of the audience could affect the accessibility and effectiveness of counterspeech. We propose a Controlled-Literacy framework using retrieval-augmented generation (RAG) with reinforcement learning (RL) to generate tailored counterspeech adapted to different health literacy levels. In particular, we retrieve knowledge aligned with specific health literacy levels, enabling accessible and factual information to support generation. We design a reward function incorporating subjective user preferences and objective readability-based rewards to optimize counterspeech to the target health literacy level. Experiment results show that Controlled-Literacy outperforms baselines by generating more accessible and user-preferred counterspeech. This research contributes to more equitable and impactful public health communication by improving the accessibility and comprehension of counterspeech to health misinformation
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