@inproceedings{tao-etal-2024-making,
    title = "Making a Long Story Short in Conversation Modeling",
    author = "Tao, Yufei  and
      Mines, Tiernan  and
      Agrawal, Ameeta",
    editor = {Hosseini-Kivanani, Nina  and
      H{\"o}hn, Sviatlana  and
      Anastasiou, Dimitra  and
      Migge, Bettina  and
      Soltan, Angela  and
      Dippold, Doris  and
      Kamlovskaya, Ekaterina  and
      Philippy, Fred},
    booktitle = "Proceedings of the 1st Worskhop on Towards Ethical and Inclusive Conversational AI: Language Attitudes, Linguistic Diversity, and Language Rights (TEICAI 2024)",
    month = mar,
    year = "2024",
    address = "St Julians, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2024.teicai-1.7/",
    pages = "42--49",
    abstract = "Conversation systems accommodate diverse users with unique personalities and distinct writing styles. Within the domain of multi-turn dialogue modeling, this work studies the impact of varied utterance lengths on the quality of subsequent responses generated by conversation models. Using GPT-3 as the base model, multiple dialogue datasets, and several metrics, we conduct a thorough exploration of this aspect of conversational models. Our analysis sheds light on the complex relationship between utterance lengths and the quality of follow-up responses generated by dialogue systems. Empirical findings suggests that, for certain types of conversations, utterance lengths can be reduced by up to 72{\%} without any noticeable difference in the quality of follow-up responses."
}