@inproceedings{parmar-mazumdar-2025-measuring,
    title = "Measuring Prosodic Richness in {LLM}-Generated Responses for Conversational Recommendation",
    author = "Parmar, Darshna  and
      Mazumdar, Pramit",
    editor = "Das, Sudhansu Bala  and
      Mishra, Pruthwik  and
      Singh, Alok  and
      Muhammad, Shamsuddeen Hassan  and
      Ekbal, Asif  and
      Das, Uday Kumar",
    booktitle = "Proceedings of the Workshop on Beyond English: Natural Language Processing for all Languages in an Era of Large Language Models",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, BULGARIA",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.globalnlp-1.14/",
    pages = "120--130",
    abstract = "This paper presents a novel framework for stylistic evaluation in conversational recommendation systems (CRS), focusing on the prosodic and expressive qualities of generated responses. While prior work has predominantly emphasized semantic relevance and recommendation accuracy, the stylistic fidelity of model outputs remains underexplored. We introduce the prosodic richness score (PRS), a composite metric that quantifies expressive variation through structural pauses, emphatic lexical usage, and rhythmic variability. Using PRS, we conduct both sentence-level and turn-level analyses across six contemporary large language models (LLMs) on two benchmark CRS datasets: ReDial, representing goal-oriented dialogue, and INSPIRED, which incorporates stylized social interaction. Empirical results reveal statistically significant differences ($p < 0.01$) in PRS between human and model-generated responses, highlighting the limitations of current LLMs in reproducing natural prosodic variation. Our findings advocate for broader evaluation of stylistic attributes in dialogue generation, offering a scalable approach to enhance expressive language modeling in CRS."
}