@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Metamo: Empowering Large Language Models with Psychological Distortion Detection for Cognition-aware Coaching
%A Hotta, Hajime
%A Le, Huu-Loi
%A Phan, Manh-Cuong
%A Nguyen, Minh-Tien
%Y Habernal, Ivan
%Y Schulam, Peter
%Y Tiedemann, Jörg
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-334-0
%F hotta-etal-2025-metamo
%X 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.
%U https://aclanthology.org/2025.emnlp-demos.66/
%P 862-872
Markdown (Informal)
[Metamo: Empowering Large Language Models with Psychological Distortion Detection for Cognition-aware Coaching](https://aclanthology.org/2025.emnlp-demos.66/) (Hotta et al., EMNLP 2025)
ACL