Think Like a Person Before Responding: A Multi-Faceted Evaluation of Persona-Guided LLM s for Countering Hate Speech. (2025)

Abstract: 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.

## introduction the rise of online hate speech remains a key concern in natural language processing (nlp) research @xcite , now intensified by social media companies shifting from factchecking to community-driven moderation. one of the ways in which we might address hate speech is by contextualizing through the use of counternarratives (cn), which can not only reinforce values like tolerance but also dispel misinformation about the target groups. however, these moderation approaches have been criticized for being labor intensive, psychologically demanding @xcite , and highly inefficient @xcite , thus increasing the risk of amplifying harmful rhetoric and misinformation that can have serious ramifications. one scalable and ethically grounded strategy to mitigate these risks is through automatic cn generation: textual responses designed to resist, contextualize or contradict hateful language @xcite foot_0 . however, this is a non-trivial task. while prior research on cn generation has emphasized dataset development, generation methods, and overall effectiveness in mitigating hate speech @xcite @xcite , little attention has been paid to affective attributes such as emotion and sentiment. affect is deeply linked to hate speech @xcite and can shape how these responses are received by different groups. to address this gap, we present a comprehensive evaluation framework for analyzing llms-generated cns across four key dimensions: (1) persona framing (vanilla, ngo professional, and a compassionate ngo professional), recognizing that delivery style can influence impact; (2) model behavior (e.g., refusal rates, verbosity and readability); (3) affective tone (sentiment and emotion); and (4) ethical risk (potential for generating hateful content). this multi-dimensional approach offers a nuanced understanding of both the capabilities and implications of using llms in high-stakes content moderation settings. contributions we conduct experiments foot_1 on two datasets using three state-of-the-art llms, ope-nai's gpt-4o-mini @xcite ); cohere's commandr-7b-12-2024 foot_2 ; and meta's llama 3.1-70b @xcite , hereafter referred as gpt, cohere, and llama respectively. each model is tested under three prompting conditions: (1) vanilla, where the model is prompted without any additional instructions beyond the default system behavior; (2) ngo-persona prompting, where the model adopts the persona of an ngo worker countering hate speech; and (3) emotion-driven persona prompting, where the ngo-persona is further refined with explicit emotional guidance. our findings reveal an inverse relationship between verbosity and readability, and also highlights the importance of a human in cn creation to ensure cns remain accessible for diverse audience. while llms demonstrate strong affective classification capabilities, they also exhibit ethical and computational vulnerabilities. these findings contribute to the growing discourse on the safe, responsible, and inclusive deployment of generative ai in high-stakes domains, particularly in developing more targeted responses to effectively countering hate speech across different population demographics. ## related work prior research on automated cn generation has largely focused on three areas: dataset development @xcite @xcite , response generation @xcite , and evaluation frameworks @xcite @xcite . conan dataset, @xcite , explored llms' effectiveness in mitigating both explicit and implicit hate speech. their evaluation, which considered content diversity, verbosity, and response quality, showed that some gpt models effectively produce humanly sound, informative responses but often struggle with detecting and generating responses for implicit hateful content. response generation and evaluation: cercas curry and rieser (2018) studied how assistants responded to abusive queries and subsequently evaluated them in a crowdworking setting (cercas curry and rieser, 2019), finding that models at the time were often accepting of sexist abuse. @xcite examined llms' ability to generate cns with vanilla prompting using gpt-2 (radford et al., 2019), dialogpt @xcite , chatgpt foot_3 , and a flant5 @xcite . their study employed three structured prompting strategies and assessed llm responses using multiple evaluation metrics, including checking toxicity levels, and readability scores. reported findings shows gpt models tend to produce contents with low readability scores and that while strategic prompting can improve narrative quality, it may also increase the risk of generating toxic responses. these concerns are echoed by @xcite , who systematically assess the propensity of llms to produce harmful content. their study uses the mt-conan dataset to evaluate eight llms (including gpt, llama, vicuna, mistral, and gemini families) under vanilla prompting conditions, employing the metahatebert model to detect hateful content. their findings revealed that certain models, particularly llama-2 and mistral, frequently generated toxic outputs even without explicit prompts. a study closely related to ours is presented by @xcite , who propose a method for generating cn that are both community-adapted and personalized for individual users. their approach leverages only the llama2-13b models, in a vanilla state and evaluates generated responses based on range of personalized and ethical criteria including toxicity, readability, relevance, and response diversity. their findings reveal a significant misalignment between automatic metrics and human judgments, suggesting that these approaches capture different dimensions of response quality. this underscores the importance of developing more nuanced and multifaceted evaluation frameworks, an insight that directly motivates our multi-dimensional assessment strategy. while these studies provide valuable insights into llm-based cn generation and evaluation, our work extends this research by introducing novel persona-and emotion-conditioned prompting strategies beyond standard vanilla prompts; sentiment, emotion, and behavioral evaluations including refusal rates, hatefulness, and readability; cross-model and cross-dataset comparisons to assess generalizability. ## methodology in this section, we describe the datasets, prompts, evaluation metrics and models used. see figure 1 for an overview of our research methodology. ## discussion automated cn generation presents a nuanced and complex challenge. our multi-faceted evaluation reveals several critical insights about llm prompting, responses and performance. model size vs performance: despite being the smallest model( 7 billion trainable parameters vs 70 and 20 billion, llama and gpt-4o-mini respectively), cohere consistently generate the most accessible cn, thus challenging the assumption that bigger models always yield better results. cost vs capability: cohere proved to be the most cost-effective model accessed through api call while llama was the most expensive. moreover, despite being open-sourced and accessible without api calls, mistral proved exponentially costly and required significantly more processing time, thus making them less feasible in lowresource settings, undermining its practicality for system scalability and deployment. dual edge nature of emotion guiding: we equally observed that prompts framed with ngo-emotion consistently produced more verbose, empathetic, and paradoxically more readable responses, suggesting that emotional context may serve as a valuable signal for generating more elaborate, persuasive and accessible responses. llm's superior understanding of contextual cues: our experiments reveal that llms like mistral exhibit a stronger ability to interpret emotional cues compared to bert-based emotion detection models, which understandably due to their significantly larger parameter size/training corpus. however, we observed that even these llm-based emotion detection models sometimes failed to identify implicit hateful cues as seen in ## future work an interesting avenue to explore would be to assess how llms responses to content in multimodal settings compared to those in a uni-modal settings. findings could help shed light on the strengths and limitations of current models in real-world moderation tasks involving multimodal contents. moreover, research has shown that fake news and hate speech amplify each other @xcite . our future work will explore dual-purpose cn designed to simultaneously correct factual inaccuracies while neutralizing harmful framing. thus helping create more efficient interventions strategies that acknowledges their inter connectedness. ## conclusion our work highlights the complexity and high stakes involved in automating cns to combat online hate speech. our findings show that while llms are capable of generating emotionally nuanced and readable responses, they often do so at the cost of verbosity and reduced accessibility, especially for people without college education. we also show that while cost-effective models like cohere hold promise for broader deployment, their behavioral unpredictability remains a challenge which needs to be investigated thoroughly before leveraging them for such tasks. as the use of generative ai expands into sensitive domains like hate speech mitigation and content moderation, ensuring that responses are not only accurate but also accessible, empathetic, and safe will be critical to fostering truly inclusive and responsible ai.