Can Large Language Models Personalize Dialogues to Generational Styles? (2025)

Abstract: We investigate how large language models (LLMs) can produce personalized dialogue responses, specifically focusing on whether they reflect linguistic styles pertaining to different generations: Baby Boomers, Generation X, Generation Y, and Generation Z. We create P-MultiWoZ, a personalized, generation-specific version of MultiWOZ 2.2, by prompting LLMs, and validate its alignment with the original dataset through automatic and human evaluations. To validate the appropriateness of generational linguistic traits, we introduce GeMoSC, a corpus of generation-annotated movie dialogues. Linguistic analysis and perplexity test suggest that P-MultiWoZ reflects patterns consistent with GeMoSC. Finally, a human evaluation reveals that annotators were able to mostly correctly identify the generation behind P-MultiWoZ dialogues, based only on a single query-reply pair.

## introduction communication style is a complex interplay of linguistic, cultural, and contextual elements that shape interactions. understanding and adapting to different styles can significantly enhance the effectiveness and satisfaction of communication @xcite . various factors influence communication styles, including age @xcite , self-concept @xcite , education @xcite , all of which shape how individuals express themselves and engage with others. several studies compare how individuals of different generations communicate @xcite , taking into account factors such as exposure to younger generations' language through social media @xcite . these distinctions are typically based on categorizations such as baby boomers (boomers, born 1 all the data and code from this paper are released under cc-4.0-by license at: https://github.com/pierbale/ p-multiwoz between 1946-64), generation x (gen x, 1965-80), generation y/millennials @xcite , and generation z @xcite . understanding communication style differences is crucial for designing adaptable communication strategies. with the rapid advancement of large language models (llms), there is growing interest in evaluating their ability to emulate human language @xcite , particularly in adapting to generational styles and preferences @xcite . by adapting to different styles, personalities, and user preferences, llms can facilitate more natural and engaging interactions @xcite @xcite . this paper examines generational communication styles as a case study of personalization in llms. we pose the crucial question: "can user profiles be integrated into llm prompts to personalize dialogue based on generational communication styles?" to understand whether llms contain implicit knowledge of how individuals from different generations express themselves and whether this knowledge allows for the personalization of dialogues. we choose the dialogue domain to address this question since user-specific linguistic traits, such as generational styles, become more apparent in less formal situations @xcite . furthermore, task-oriented dialogues are particularly well-suited for this purpose, as they emphasize the importance of aligning system outputs with user preferences. therefore, we use the multiwoz 2.2 dataset @xcite as a testbed. we create generation-specific speaker profiles and paraphrase multiwoz dialogues through llms, resulting in the personalized corpus p-multiwoz. the final p-multiwoz consists of 240 dialogues (60 per generation variant: p-multiwoz b, x, y, z), with a total of 14, 720 user-system turns, where both user and system utterances are style-adapted. additionally, we build a custom corpus, gemosc (generational movie script corpus), containing utterances of movie characters belonging to different generations, to conduct detailed linguistic analyses and comparisons. we assess content consistency of p-multiwoz with the original multiwoz dataset and evaluate whether the generation-specific paraphrases effectively reflect generational styles, through automatic analyses and human annotations. the results show that p-multiwoz retains the same content as the original dataset and shares linguistic patterns corresponding to the generations represented in the movie corpus. furthermore, human annotators were able to recognize whether a conversation extracted from p-multiwoz belonged to a specific generation in most cases. this demonstrates that llms can personalize conversations based on generational communication styles, reflecting linguistic patterns that align with those of real individuals. ## related work numerous studies have investigated the factors contributing to effective conversations @xcite @xcite , highlighting the importance of personalizing dialogue systems to enhance their capabilities. notably, conversational dynamics are significantly influenced by factors such as an individual's knowledge @xcite , physical abilities @xcite and age @xcite . moreover, successful interactions often depend on the ability of interlocutors to adapt to their dialogue counterpart @xcite , an issue mostly overlooked in current dialogue systems @xcite . several approaches to conversational models increasingly aim to integrate personal characteristics-such as age, gender, geographical location, and physical abilities-through both explicit mechanisms @xcite and implicit modeling @xcite @xcite . llms have emerged as powerful tools for generating and personalizing text based on diverse styles, by prompting strategies and fine-tuning. recent studies have demonstrated their ability to adapt linguistic output to specific stylistic preferences, making them valuable for applications ranging from automated writing assistance to dialogue systems @xcite . while corpora designed to embed personal information into conversations are emerging, creating opportunities to enhance dialogue personalization @xcite , current research often overlooks user-specific traits such as generational conversational patterns. our work highlights the importance of adapting dialogue systems to better align with users' characteristics. ## p-multiwoz to evaluate llms' ability to produce generationspecific language, we create p-multiwoz, a personalized version of the multiwoz 2.2 @xcite task-oriented dialogue dataset. we first prompt an llm to define user profiles that reflect the distinctive communication styles of boomers, gen x, gen y, and gen z. these profiles guide further prompting of llms to simulate generational communication styles and generate paraphrased versions of a subset of multiwoz 2.2 dialogues. to ensure content consistency, we evaluate the paraphrased dialogues through an automatic analysis using llms, complemented by a manual annotation of a random small subset of these dialogues. based on these evaluations, we identify the most effective llm for generating the final paraphrased dialogues. we then analyze how closely these dialogues align with the expected styles of their corresponding generations (see sections 4 and 5). multiwoz 2.2 @xcite ) is a largescale, multi-domain, human-human conversational corpus of over 10, 000 dialogues covering multiple domains such as booking hotels, restaurants, and transportation services, making it one of the most comprehensive and challenging datasets for building robust dialogue systems capable of handling complex, multi-turn interactions across different contexts @xcite . ## evaluating generational differences to check how well the different p-multiwoz corpora reflect generational differences, we built a new dataset based on movie scripts that contain dialogues from characters belonging to different generations. this dataset, called generational movie script corpus (gemosc) is used to (1) linguistically compare and identify common features with the p-multiwoz corpora, (2) fine-tune generationspecific llms and calculate their perplexity on the various p-multiwoz corpora. this is to verify that, e.g., the model finetuned on the boomer gemosc corpus shows a lower perplexity when evaluated on the boomer corpus from p-multiwoz. gemosc we started with the publicly available movie scripts corpus dataset, foot_5 which includes a comprehensive list of films and their scripts, organized by character. to assign characters to a generation, one of the authors manually annotated selected scripts, classifying characters' gender and year of birth using the online imdb ## human evaluation to further assess whether p-multiwoz adapts to generational communication styles, we conducted a human evaluation. participants were tasked with identifying the generation of p-multiwoz conversations and providing feedback. we recruited 26 english-proficient volunteers via mailing lists and supplied them with a qualtrics foot_8 questionnaire, estimating a 15-minute completion time. participants provided informed consent and answered profiling questions on gender and birth year. of the 26 participants, 19 were male, 7 female; 11 were from gen z, 11 gen y, and 4 gen x. each participant completed 5 questions, each featuring 5 pairs of conversation excerpts contrasting user-system turns from two generations. participants identified the perceived generation or selected "not sure." figure 1 shows a confusion matrix summarizing the results. a pattern emerges in which participants were able to identify the correct generation more consistently than the others. this was particularly true for gen z, where participants more easily recognized these dialogues. the highest confusion occurred between consecutive generations, e.g., boomer conversations were often attributed to gen x. participants' comments supported these findings, highlighting subtle differences between consecutive generations. the predominance of gen z and gen y participants likely facilitated recognition of their own generations. it is important to note, however, that the classification accuracy observed in figure 1 does not directly reflect the overall quality of the p-multiwoz dataset. in fact, distant generations exhibit greater discrimination power, while most misclassifications occur between adjacent generations, suggesting that certain stylistic overlaps exist across generational boundaries, as shown in indeed, from this analysis, we can conclude that the llm-generated p-multiwoz data capture generational communication styles consistent with those of real individuals. ## limitations this study represents a significant first step toward the generational stylistic analysis of language models, although it has several limitations. the models employed are small due to computational constraints, yet they yield interesting and promising results. mid-sized and larger llms may offer higher performance but also incur substantially greater computational costs. the p-multiwoz dataset, though limited in size, has shown potential in emulating certain generational linguistic features, and we plan to expand it in the future to make it a 1:1 replica of multiwoz, ensuring greater representa-tiveness. the gemosc dataset, despite being small as well, represents what we consider an original and important contribution, born from an extensive manual annotation process. we chose to create this dataset instead of using pre-existing conversational data, such as that extracted from social media, which could compromise user privacy, as part of an ethical choice. our annotation and selection guidelines (see appendix e) ensure the texts conform to the appropriate generational styles. paraphrasing existing dialogues instead of producing free-form new dialogues may constrain the capture of generational patterns beyond style, such as openness. while this mitigates hallucinations and reduces stereotype reinforcement, it may also limit the representation of deeper generational aspects in dialogue acts. lastly, the human experimentation involved an unbalanced number of participants representing different generations, with the boomer generation being absent. despite these limitations, the results still confirm the potential of llms in emulating distinctive generational styles, allowing for further research in this direction. ## ethical considerations the human evaluation campaign, crucial for manually verifying the personalization of p-multiwoz, involved several anonymous volunteer annotators with the sole requirement of being proficient in english. the annotation task took approximately 15 minutes to complete. furthermore, before participating in the experiment, they signed an informed consent form explicitly stating, among other things: i) to be aware of the objectives of this research; ii) to participate on a voluntary basis; iii) to be of legal age; iv) to be aware that the study complies with current data processing and protection regulations, both at the national and eu level; v) to be aware of the possibility of withdrawing from the study at any time, without explanation, without any penalty, and with the assurance that their data would not be used. moreover, we acknowledge that personalizing llms based on generational traits risks reinforcing stereotypes, leading to biased assumptions and unfair treatment. to mitigate these risks, transparency measures, bias audits, and fairness evaluations are essential. our future work will focus on balancing personalization with ethical safeguards to ensure inclusivity and prevent unintended harm.