Enhancing LLM -Based Persuasion Simulations with Cultural and Speaker-Specific Information (2025)

Abstract: Large language models (LLMs) have been used to synthesize persuasive dialogues for studying persuasive behavior. However, existing approaches often suffer from issues such as stance oscillation and low informativeness. To address these challenges, we propose reinforced instructional prompting, a method that ensures speaker characteristics consistently guide all stages of dialogue generation. We further introduce multilingual prompting, which aligns language use with speakers’ native languages to better capture cultural nuances. Our experiments involving speakers from eight countries show that continually reinforcing speaker profiles and cultural context improves argument diversity, enhances informativeness, and stabilizes speaker stances. Moreover, our analysis of inter-group versus intra-group persuasion reveals that speakers engaging within their own cultural groups employ more varied persuasive strategies than in cross-cultural interactions. These findings underscore the importance of speaker and cultural awareness in LLM-based persuasion modeling and suggest new directions for developing more personalized, ethically grounded, and culturally adaptive LLM-generated dialogues.

## introduction recent studies have demonstrated the effectiveness of large language models (llms) in multi-agent communication environments for simulating persuasion behaviors @xcite . persuasion is a complex form of human communication that relies on strategic language use and strong personal opinions. while llms have shown promise in generating persuasive dialogues, existing simulations * equal contribution. this paper explores whether large language models (llms) can generate persuasion dialogues that adapt to speakers' backgrounds, producing more diverse persuasive strategies and supporting evidence that better align with speaker profiles. specifically, we examine how incorporating and consistently reinforcing speaker profiles influences the diversity of persuasion strategies, stance consistency, and the informativeness and cultural alignment of supporting arguments. to this end, we introduce two enhancements to the baseline persuasion simulation framework proposed by @xcite : (1) personal-profile-infused persuasion simulation, where speaker characteristics are explicitly embedded in the initial prompts to align generated responses with the speakers' demographics. (2) personal-profile-reinforced persuasion simulation, where speaker profiles are repeatedly emphasized throughout the dialogue to guide each generation step. for both approaches, we further explore a multilingual variant in which prompts and responses are generated in each speaker's native language. this allows us to assess the effect of stronger cultural cues on the quality and authenticity of the generated dialogues. after generating the persuasion dialogues, we conduct a comprehensive manual analysis to: (1) assess the linguistic fluency and logical consistency of the outputs, (2) evaluate whether the framework successfully aligns generated dialogues with speaker backgrounds and maintains assigned stances, even when they deviate from the ideological biases typically exhibited by llms, and (3) examine persuasion strategy diversity within and across cultural groups, determining how speakerspecific prompting affects argumentation styles. additionally, we analyze how language choice influences generation quality and how stance extremity affects the formulation of arguments. our results demonstrate that incorporating speaker profiles and reinforcing prompts significantly enhance persuasion simulation quality by: (1) generating more informative and novel arguments, (2) increasing the likelihood of concrete justifications and evidence, and (3) producing a broader range of persuasive strategies compared to baseline framework. we also observe a trade-off between linguistic diversity and strategic variation: while enforcing native language use improves argument novelty, it somewhat reduces strategy diversity. nevertheless, this does not alter our primary conclusion that speaker awareness significantly improves the realism and effectiveness of llm-generated persuasion dialogues. by bridging this gap, our speaker-infused persuasion framework expands the scope of computational persuasion research, enabling deeper investigations into argumentation strategies, cultural influences, and ideological positioning. this work contributes to more realistic, context-aware aigenerated dialogues, with potential applications in social science research, ai-assisted debate systems, and cross-cultural communication modeling. ## speaker-infused persuasim we follow @xcite and employ a multi-llm communication framework to generate persuasive dialogues between two speakers with given background information. the framework consists of two dialogue generation agents (for persuader and persuadee), an utterance quality monitor and a language refinement agent to enhance utterancelevel quality, and a global regulation agent to manage dialogue flow. we refer to this framework as persuasim-orig and provide the corresponding agent prompts in appendix b.1. for personal information injection into persuasim-orig, we modify the initial prompts to the dialogue generation agents and create persuasim-infused. prompts for persuasim-infused are shown in appendix b.2. building upon persuasim-infused, we introduce persuasim-reinforced, which further emphasizes the necessity for dialogue generation agents to align with the assigned personal backgrounds in every round of generation. the utterance quality monitor please keep playing the role of: {profile}. and keep speaking {language} to reply to the persudaee/persuader. only return your response without any other words. the persuadee/persuader said: figure 2: additional instructions to the dialogue generation agents in persuasim-reinforced (prompts 3.0). we additionally designed persuasim-infused-multiling and persuasim-reinforced-multiling to investigate the impacts of language choice in the prompts. in these settings, prompts to each dialogue generation agent are translated into the official language of the agent's specified country of origin, and they are required to generate in that language as well. we made two modifications: (1) translating the initial settings (figure b7 ) and passing prompts (figure 2 ) into the target languages. (2) adding an additional monitoring filter (3.2 in figure 1 ) to verify whether the generated utterance is in the intended language. if it is not, the utterance is revised accordingly. the corresponding prompts are shown in figure 4 . ## topic selection & dialogue generation using the persuasion simulation frameworks detailed in section 2, we identify topics with significant cross-country differences in viewpoints ( ## validations and result analyses after obtaining persuasion dialogues, we conduct comprehensive manual annotations to evaluate the generation quality of the persuasion simulation framework with controlled participant profiles. our evaluation focuses on the framework's ability to: (a) generate high-quality overall dialogues (section 4.1), (b) enhance the concreteness of supporting arguments (section 4.2), and (c) demonstrate diversity in persuasion strategy preferences (section 4.3). additionally, we examine how inter-group dialogues differ from intra-group dialogues across these measures (section 4.4). ## conclusion and future work this paper presents speaker-infused prompting as a means to enhance the informativeness and stance consistency of llm-generated persuasion dialogues. we introduced two key contributions: (1) persuasim-reinforced, a multi-agent persuasion simulation framework that reinforces speaker characteristics at every generation round, and (2) a multilingual prompting extension, which further strengthens cultural alignment in persuasion strategy selection and evidence generation. our experimental results demonstrate that reinforcing speaker profiles significantly improves the quality of generated dialogues by: (1) aligning speaker stances with their cultural and demographic back-grounds, thereby enhancing contextual relevance; future work can extend these methods to simulate other forms of abstract human behavior or complex multi-party interactions.