“ I understand your perspective”: LLM Persuasion through the Lens of Communicative Action Theory (2025)

Abstract: Large Language Models (LLMs) can generate high-quality arguments, yet their ability to engage in *nuanced and persuasive communicative actions* remains largely unexplored. This work explores the persuasive potential of LLMs through the framework of Jürgen Habermas’ Theory of Communicative Action. It examines whether LLMs express illocutionary intent (i.e., pragmatic functions of language such as conveying knowledge, building trust, or signaling similarity) in ways that are comparable to human communication.We simulate online discussions between opinion holders and LLMs using conversations from the persuasive subreddit *ChangeMyView*. We then compare the likelihood of illocutionary intents in human-written and LLM-generated counter-arguments, specifically those that successfully changed the original poster’s view. We find that all three LLMs effectively convey illocutionary intent — often more so than humans — potentially increasing their anthropomorphism. Further, LLMs craft responses that closely align with the opinion holder’s intent, a strategy strongly associated with opinion change. Finally, crowd-sourced workers find LLM-generated counter-arguments more *agreeable* and consistently prefer them over human-written ones. These findings suggest that LLMs’ persuasive power extends beyond merely generating high-quality arguments. On the contrary, training LLMs with human preferences effectively tunes them to mirror human communication patterns, particularly nuanced communicative actions, potentially increasing individuals’ susceptibility to their influence.

## introduction public discourse is essential for shaping opinions, exposing individuals to diverse perspectives, and challenging existing beliefs. today, much of this discourse takes place online, especially on social media platforms @xcite , where aigenerated and edited content is becoming increasingly prevalent @xcite . what role does ai play in public discourse, particularly in shaping and changing opinions? recent research highlights concerning trends in this regard, suggesting that humans may be susceptible to undue influence from llms on topics ranging from politics to social and environmental issues. a key challenge is that humans often struggle to recognize ai-generated text due to cognitive shortcuts, such as associating first-person pronouns or family-related topics with human authorship @xcite . additionally, llms can infer the psychological dispositions of social media users @xcite and provide sycophantic responses (i.e., convincing, well-written responses that confirm a user's mistaken beliefs), reinforcing confirmation bias @xcite . consider the conversation illustrated in figure 1 (white boxes). a user expresses their opinion on atheists in western nations using a conflicting statement "atheists are the worst". two users reply: the first shares knowledge on the topic by listing facts and anecdotes, while the second simply states their opinion. framing an argument within such illocutionary intents (social dimensions; see @xcite and further examples in rq2 what are the (dis-)similarities between the social dimensions expressed in successful humanwritten arguments and those generated by llms? rq3 to what extent can llms capture interactional dynamics of social dimensions between opinion holders and successful commenters? rq4 do people prefer human-written opinionchanging arguments over llm-generated ones? and do their preferences align more closely with the strategies used by humans or by llms? to address these research questions, we simulate conversations between human opinion holders and llms using discussions from the persuasive online forum /r/changemyview (cmv). specifically, we generate counter-arguments for cmv posts (see blue box in figure 1 ) using three llms and compare the likelihood of these texts expressing nine social dimensions to that of human-written opinionchanging comments. we find that llm-generated texts contain more social dimensions than human counter-arguments and consistently express trust in the opinion holder ( §5.1). examining the interaction dynamics, we observe that llms exhibit a capacity to model the communicative intent of the opinion holder and craft replies that mirror persuasive human strategies -in the case of gpt-3.5-turbo, even more strongly than the opinionchanging human responses ( §5.3). finally, in a direct comparison between human-written opinionchanging comments and llm-generated ones, crowdworkers found the ai-generated responses more agreeable and overwhelmingly preferable. in fact, in 83% of cases, they judged llm-generated arguments as more likely to change the opinion holder's view ( §5.4). our work makes two key contributions. first, in the field of persuasive ai, we show that llms' persuasive capabilities extend beyond generating high-quality arguments. this underscores the need to study not just what ai "says" but how it engages with and adapts to human discourse, shaping opinion formation in new and unexpected ways. second, regarding communication dynamics, we build on findings of @xcite , extending them to ai-driven interactions. our results suggest that human preferences are influenced by more than just the quality of arguments, highlighting the complex interplay between social intent and persuasion. ## related work much of the literature on llm persuasiveness is focused on humans' perceptions of it, often comparing llms to humans. @xcite examined pro-vaccination messages created by both ai models and human authors, discovering that aigenerated messages were often perceived as more persuasive, except when they were explicitly la- dimension description example knowledge exchange of ideas or information; learning, teaching history shows the benefits of vaccinations outweigh the risks. power having power over behavior and outcomes of another the only rights which exist in reality are legal rights. status conferring status, appreciation, gratitude, or admiration i have nothing but the utmost respect for service men. trust will of relying on the actions or judgments of another i understand your perspective. support giving emotional or practical aid and companionship i'd feel sympathy for their situation. similarity shared interests, motivations or outlooks i'm glad to know we agree on this. identity shared sense of belonging to the same group they are members of their tribe (...) fun experiencing leisure, laughter, and joy realize how much you sound like chamberlin when hitler (...) conflict contrast or diverging views atheists are the worst for this (...) personalization plays a key role in the persuasiveness of models, as tailoring content to align with individuals' psychological traits can significantly influence their online behavior @xcite . @xcite demonstrated that personalized messages crafted by chat-gpt were significantly more persuasive than nonpersonalized ones. @xcite found that assigning different personas to llms could significantly alter the text's persuasiveness. similarly, costello et al. (2024) engaged conspiracy believers in personalized, evidence-based dialogues with gpt-4-turbo and reported a reduction in conspiracy beliefs by 20%. in a controlled study by @xcite , concerns were raised about the implications of personalized persuasion for the governance of social media and online environments. lastly, @xcite analyzed human preference data showing that when ai responses matched users' views, they were more likely to be preferred. their research also suggested that both humans and preference models sometimes favored sycophantic responses over correct ones, highlighting a potential issue in ai behavior. while a small body of research has explored whether llms are persuasive to humans, there is a lack of literature explaining why they are persuasive, particularly in relation to established theoretical frameworks. closely related to our work, @xcite examined the effectiveness of llm-generated arguments when designed to convey specific social dimensions. their findings showed that arguments combining factual knowledge, trust markers, expressions of support, and status cues were rated as most persuasive by both humans and ai agents. notably, humans found knowledge-based arguments particularly compelling, highlighting the importance of factual support in persuasive messaging. however, their study was conducted in a synthetic persuasion dialogue setting, leaving open the question of whether llms naturally incorporate social dimensions when countering real-world social media posts without being explicitly prompted. furthermore, no direct comparison has been made between llm-generated and human-written messages regarding the presence of social dimensions or their effects on human perceptions of persuasiveness. ## data this study draws on discussions from the subreddit /r/changemyview, a forum where users share their opinions and invite others to challenge them. when a commenter successfully persuades the original poster (op) to reconsider their stance, they receive a "delta" (∆) as a reward, signaling a successful change of opinion (see ∆ example in figure 1 ). we use the publicly available changemyview (cmv) corpus collected by @xcite . it is a well-established dataset that underwent multiple further analyses @xcite . crucially, since the dataset consists of posts and comments written before 2016, we can be certain that none were gen-erated by llms. since we do not train models on this data, we merge the training and held-out portions for analysis, which consists of 20, 626 posts and 1, 260, 266 comments. ## results in the following, we analyze social dimensions in counter-argument comments generated by three selected models: llama2-7b, mistral-7binstruct, and gpt-3.5-turbo. ## conclusions and discussion as llm-generated content becomes increasingly prevalent online, understanding its influence on human opinions becomes essential. to move towards this understanding, we examined llm persuasiveness through the lens of communicative actiona theoretical framework that views language as a means of expressing social intent through reasoned dialogue aimed at achieving mutual understanding, which is crucial for human opinion change. using real-world persuasive discourse as a reference point, we showed that models effectively convey illocutionary intent -often more frequently and densely than humans. notably, all three models we analyzed consistently express trust in their conversation partners, potentially affirming their views and contributing to perceived biased likability @xcite . while all three models show similar patterns of social intent reciprocity to that of the successful human comments, the postcomment interactions of gpt-3.5-turbo-generated arguments reveal even stronger patterns of reci-procity -a behavior closely linked to opinion change @xcite . finally, llms employ rhetorical strategies that differ from those found in human-written opinionchanging arguments -especially in conveying the two dimensions previously linked to successful persuasion: knowledge and similarity. importantly, crowdworkers not only find these llm-generated arguments more agreeable but also consistently prefer them over human-written opinion-changing ones. while further research is needed to identify which dimensions drive their decision the most, our findings show that llms are capable of engaging in nuanced communicative actions, potentially shaping human opinions in ways not yet fully understood. moreover, the varying patterns observed between instruction and chat models suggests that alignment training for llms may not only reinforce existing human biases but also increase individuals' susceptibility to ai-driven influence on critical issues like politics, social justice, and the environment. in light of these concerns, we urge further research into alignment training and its broader effects on public discourse and opinion formation.