@inproceedings{hotta-etal-2025-metamo,
    title = "Metamo: Empowering Large Language Models with Psychological Distortion Detection for Cognition-aware Coaching",
    author = "Hotta, Hajime  and
      Le, Huu-Loi  and
      Phan, Manh-Cuong  and
      Nguyen, Minh-Tien",
    editor = {Habernal, Ivan  and
      Schulam, Peter  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-demos.66/",
    pages = "862--872",
    ISBN = "979-8-89176-334-0",
    abstract = "We demonstrate Metamo, a browser-based dialogue system that transforms an off-the-shelf large language model into an empathetic coach for everyday workplace concerns. Metamo introduces a light, single-pass wrapper that first identifies the cognitive distortion behind an emotion, then recognizes the user{'}s emotion, and finally produces a question-centered reply that invites reflection, all within one model call. The wrapper keeps the response time below two seconds in the API, yet enriches the feedback with cognitively grounded insight. A front-end web interface renders the detected emotion as an animated avatar and shows distortion badges in real time, whereas a safety layer blocks medical advice and redirects crisis language to human hotlines. Empirical tests on public corpora confirmed that the proposed design improved emotion{-}recognition quality and response diversity without sacrificing latency. A small user study with company staff reported higher perceived empathy and usability than a latency{-}matched baseline. Metamo is model-agnostic, illustrating a practical path toward cognition-aware coaching tools."
}