Speaking at the Right Level: Literacy-Controlled Counterspeech Generation with RAG - RL (2025)

Abstract: 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.

## introduction health misinformation online poses a serious risk to public health as it can mislead people, cause bad health choices, and reduce trust in doctors and medical advice @xcite . a user on reddit once claimed that "i won't take a mammogram because it is the squishing that causes cancer." such health misinformation not only misleads individuals into avoiding appropriate medical actions but also increases public confusion @xcite . therefore, it is essential to curb the spread of health misinformation. social media users actively participate in combating health misinformation by posting counterspeech @xcite @xcite the covid-19 vaccine contains pig and monkey dna. the vaccine' s composition is transparently disclosed, and its ingredients are well-documented. the vaccine contains no pig or monkey dna. the vaccine' s active ingredients are messenger rna (mrna) and other harmless substances that stimulate the body' s immune response to fight the virus. the covid-19 vaccine does not contain any pig or monkey dna. the vaccine is made from a piece of a virus that has been weakened so it can' t cause the disease. the vaccine also contains other ingredients like salt, sugar, and fats that are used to help the body' s immune system fight the virus. the covid-19 vaccine does not contain any animal dna, like pig or monkey dna. it is made from a tiny, weak part of the virus that cannot make you sick. it also has simple things like salt and sugar to help it work. ## cross evaluation we employ two evaluation methods to assess our proposed framework. firstly, we test our methods on check-covid and misinfocorrect to examine their generalization ability. these two datasets reflect different sources of misinformation: twitter and online news media, respectively. second, we perform cross-evaluation to analyze how users with a particular health literacy level respond to counterspeech generated for other literacy levels. this allows us to demonstrate that our method is more effective when tailored to the intended target health literacy level. however, there are notable exceptions where users prefer counterspeech designed for different literacy levels. for example, in the instructional prompt setting, medium users rated low-level coun-terspeech slightly higher (0.75) than medium-level (0.74). in the rag setting, medium users rated both low and medium-level counterspeech equally (0.73). likewise, in the controlled-literacy setting, medium users showed a higher preference for lowlevel responses (0.74) compared to medium-level (0.73). these findings suggest that while aligning counterspeech with user literacy level generally yields the best user preference outcomes, users may sometimes prefer responses with a lower readability level, possibly due to better accessibility. this inspires us to further optimize our generation strategy by aligning responses more closely with the lower end of the target health literacy range in the future. ## qualitative analysis to understand why the optimized counterspeech achieves higher performance across politeness, target distance, and factual accuracy, yet still falls short of a perfect user preference score, we conduct a qualitative analysis with human experts. we engage two experts in misinformation studies to review and analyze the reasons, identify their strengths, and highlight remaining limitations. we randomly select 50 samples from the best-performing model, controlled-literacy, using llama3.1-8b-instruct, for high health literacy level of users. they summarize several key elements that make the counterspeech achieve high performance: (1) evidence-based reasoning: most of the counterspeech cite scientific sources (cdc, who, peerreviewed studies) and explain the details, which matches the expectations of rational justification for high health literacy level of users. (2) precise terminology. the counterspeech uses domain-specific vocabulary, such as "pathogenesis," "clinical trials" and so on. (3) structured argumentation. the counterspeech often follows a consistent structure: @xmath0, satisfying expectations for logical coherence and depth. however, our best-performing responses also exhibit several shortcomings that need further improvement. (1) repetitive openings. many counterspeech begin with similar phrases (e.g., "thank you for sharing..."), which lack lexical variety. it tends to be less human-like, potentially decreasing the effectiveness of counterspeech. (2) heavy information density. while users with high health liter-acy generally prefer more technical content, some responses are overly dense and packed with information. it may diminish clarity and fatigue readers. ## conclusion we propose a controlled-literacy method to create counterspeech that matches users' health literacy levels when addressing health misinformation. this framework enables the integration of rag and rl to generate accessible and audience-appropriate counterspeech. to control the evidence used during generation, we construct a knowledge base that contains diverse evidence and filter the evidence for each target group after retrieval. our reward design combines user preference and readability, ensuring the generated content aligns with the health literacy needs of different user groups. experimental results show that controlled literacy produces counterspeech that is more accessible, polite, user-preferred by intended groups, and factually accurate. furthermore, cross-generalization experiments demonstrate the robustness of our method across various types of health misinformation.