@inproceedings{skenderi-etal-2025-team,
    title = "Team Conversational {AI}: Introducing Effervesce",
    author = {Skenderi, Erjon  and
      Laaksonen, Salla-Maaria  and
      Huhtam{\"a}ki, Jukka},
    editor = {H{\"a}m{\"a}l{\"a}inen, Mika  and
      {\"O}hman, Emily  and
      Bizzoni, Yuri  and
      Miyagawa, So  and
      Alnajjar, Khalid},
    booktitle = "Proceedings of the 5th International Conference on Natural Language Processing for Digital Humanities",
    month = may,
    year = "2025",
    address = "Albuquerque, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.nlp4dh-1.43/",
    pages = "502--513",
    ISBN = "979-8-89176-234-3",
    abstract = "Group conversational AI, especially within digital workspaces, could potentially play a crucial role in enhancing organizational communication. This paper introduces Effervesce, a Large Language Model (LLM) powered group conversational bot integrated into a multi-user Slack environment. Unlike conventional conversational AI applications that are designed for one-to-one interactions, our bot addresses the challenges of facilitating multi-actor conversations. We first evaluated multiple open-source LLMs on a dataset of 1.6k group conversation messages. We then fine-tuned the best performing model using a Parameter Efficient Fine-Tuning technique to better align Effervesce with multi-actor conversation settings. Evaluation through workshops with 40 participants indicates positive impacts on communication dynamics, although areas for further improvement were identified. Our findings highlight the potential of Effervesce in enhancing group communication, with future work aimed at refining the bot{'}s capabilities based on user feedback."
}