Aligning to Adults Is Easy, Aligning to Children Is Hard: A Study of Linguistic Alignment in Dialogue Systems
Dorothea French, Sidney K. D’Mello, Katharina von der Wense · 2024
During conversations, people align to one another over time, by using similar words, concepts, and syntax.This helps form a shared understanding of the conversational content and is associated with increased engagement and satisfaction.It also affects conversation outcomes: e.g., when talking to language learners, an above normal level of linguistic alignment of parents or language teachers is correlated with faster language acquisition.These benefits make human-like alignment an important property of dialogue systems, which has often been overlooked by the NLP community.In order to fill this gap, we ask: (RQ1) Due to the importance for engagement and satisfaction, to what degree do state-of-the-art dialogue systems align to adult users?(RQ2) With a potential application to child language acquisition in mind, do systems, similar to parents, show high levels of alignment during conversations with children?Our experiments show that Chat-GPT aligns to adults at roughly human levels, while Llama2 shows elevated alignment.However, when responding to a child, both systems' alignment is below human levels.