@inproceedings{stricker-paroubek-2024-shot,
    title = "A Few-shot Approach to Task-oriented Dialogue Enhanced with Chitchat",
    author = "Stricker, Armand  and
      Paroubek, Patrick",
    editor = "Kawahara, Tatsuya  and
      Demberg, Vera  and
      Ultes, Stefan  and
      Inoue, Koji  and
      Mehri, Shikib  and
      Howcroft, David  and
      Komatani, Kazunori",
    booktitle = "Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2024",
    address = "Kyoto, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2024.sigdial-1.50/",
    doi = "10.18653/v1/2024.sigdial-1.50",
    pages = "590--602",
    abstract = "Large language models (LLMs) tuned for chat have recently been adopted for few-shot end-to-end task-oriented dialogue (TOD), with some success. To further assess this method, we conduct experiments on two, more complex, task-oriented benchmarks that integrate elements of chitchat into the conversation. We enhance a few-shot baseline by adding zero-shot chitchat detection and implementing \textit{function calling} for dialogue state tracking (DST). We focus on this step in the task-oriented pipeline as it comes first, and errors due to added chitchat at this stage have the most impact on end-to-end performance. We find that this prompting method shows increased resilience to mixed-mode inputs and our enhanced pipeline allows for natural inter-mode conversations, as assessed through human evaluation. Our findings also suggest that the performance gap between few-shot prompting for TOD and supervised task-specific models is narrowing."
}