Can Large Language Models Personalize Dialogues to Generational Styles?
Pier Felice Balestrucci, Ondrej Dusek, Luca Anselma, Alessandro Mazzei
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.
BibTeX
@inproceedings{emnlp2025_canlargelanguage,
title = {Can Large Language Models Personalize Dialogues to Generational Styles?},
author = {Pier Felice Balestrucci and Ondrej Dusek and Luca Anselma and Alessandro Mazzei},
booktitle = {EMNLP 2025},
year = {2025}
}