@inproceedings{inoue-etal-2025-llm,
    title = "An {LLM} Benchmark for Addressee Recognition in Multi-modal Multi-party Dialogue",
    author = "Inoue, Koji  and
      Lala, Divesh  and
      Elmers, Mikey  and
      Ochi, Keiko  and
      Kawahara, Tatsuya",
    editor = "Torres, Maria Ines  and
      Matsuda, Yuki  and
      Callejas, Zoraida  and
      del Pozo, Arantza  and
      D'Haro, Luis Fernando",
    booktitle = "Proceedings of the 15th International Workshop on Spoken Dialogue Systems Technology",
    month = may,
    year = "2025",
    address = "Bilbao, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.iwsds-1.36/",
    pages = "330--334",
    ISBN = "979-8-89176-248-0",
    abstract = "Handling multi-party dialogues represents a significant step for advancing spoken dialogue systems, necessitating the development of tasks specific to multi-party interactions. To address this challenge, we are constructing a multi-modal multi-party dialogue corpus of triadic (three-participant) discussions. This paper focuses on the task of addressee recognition, identifying who is being addressed to take the next turn, a critical component unique to multi-party dialogue systems. A subset of the corpus was annotated with addressee information, revealing that explicit addressees are indicated in approximately 20{\%} of conversational turns. To evaluate the task{'}s complexity, we benchmarked the performance of a large language model (GPT-4o) on addressee recognition. The results showed that GPT-4o achieved an accuracy only marginally above chance, underscoring the challenges of addressee recognition in multi-party dialogue. These findings highlight the need for further research to enhance the capabilities of large language models in understanding and navigating the intricacies of multi-party conversational dynamics."
}