@inproceedings{jia-etal-2024-leveraging,
    title = "Leveraging {LLM}s for Dialogue Quality Measurement",
    author = "Jia, Jinghan  and
      Komma, Abi  and
      Leffel, Timothy  and
      Peng, Xujun  and
      Nagesh, Ajay  and
      Soliman, Tamer  and
      Galstyan, Aram  and
      Kumar, Anoop",
    editor = "Yang, Yi  and
      Davani, Aida  and
      Sil, Avi  and
      Kumar, Anoop",
    booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2024.naacl-industry.30/",
    doi = "10.18653/v1/2024.naacl-industry.30",
    pages = "359--367",
    abstract = "In task-oriented conversational AI evaluation, unsupervised methods poorly correlate with human judgments, and supervised approaches lack generalization. Recent advances in large language models (LLMs) show robust zero- and few-shot capabilities across NLP tasks. Our paper explores using LLMs for automated dialogue quality evaluation, experimenting with various configurations on public and proprietary datasets. Manipulating factors such as model size, in-context examples, and selection techniques, we examine {\textquotedblleft}chain-of-thought{\textquotedblright} (CoT) reasoning and label extraction procedures. Our results show that (1) larger models yield more accurate dialogue labels; (2) algorithmic selection of in-context examples outperforms random selection,; (3) CoT reasoning where an LLM is asked to provide justifications before outputting final labels improves performance; and (4) fine-tuned LLMs outperform out-of-the-box ones. In addition, we find that suitably tuned LLMs exhibit high accuracy in dialogue evaluation compared to human judgments."
}