@inproceedings{haydarov-etal-2025-towards,
title = "Towards {AI}-Assisted Psychotherapy: Emotion-Guided Generative Interventions",
author = "Haydarov, Kilichbek and
Mohamed, Youssef and
Goldenhersch, Emilio and
OCallaghan, Paul and
Li, Li-jia and
Elhoseiny, Mohamed",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1664/",
pages = "32724--32743",
ISBN = "979-8-89176-332-6",
abstract = "Large language models (LLMs) hold promise for therapeutic interventions, yet most existing datasets rely solely on text, overlooking non-verbal emotional cues essential to real-world therapy. To address this, we introduce a multimodal dataset of 1,441 publicly sourced therapy session videos containing both dialogue and non-verbal signals such as facial expressions and vocal tone. Inspired by Hochschild{'}s concept of emotional labor, we propose a computational formulation of \textit{emotional dissonance}{---}the mismatch between facial and vocal emotion{---}and use it to guide emotionally aware prompting. Our experiments show that integrating multimodal cues, especially dissonance, improves the quality of generated interventions. We also find that LLM-based evaluators misalign with expert assessments in this domain, highlighting the need for human-centered evaluation. Data and code will be released to support future research."
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<abstract>Large language models (LLMs) hold promise for therapeutic interventions, yet most existing datasets rely solely on text, overlooking non-verbal emotional cues essential to real-world therapy. To address this, we introduce a multimodal dataset of 1,441 publicly sourced therapy session videos containing both dialogue and non-verbal signals such as facial expressions and vocal tone. Inspired by Hochschild’s concept of emotional labor, we propose a computational formulation of emotional dissonance—the mismatch between facial and vocal emotion—and use it to guide emotionally aware prompting. Our experiments show that integrating multimodal cues, especially dissonance, improves the quality of generated interventions. We also find that LLM-based evaluators misalign with expert assessments in this domain, highlighting the need for human-centered evaluation. Data and code will be released to support future research.</abstract>
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%0 Conference Proceedings
%T Towards AI-Assisted Psychotherapy: Emotion-Guided Generative Interventions
%A Haydarov, Kilichbek
%A Mohamed, Youssef
%A Goldenhersch, Emilio
%A OCallaghan, Paul
%A Li, Li-jia
%A Elhoseiny, Mohamed
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F haydarov-etal-2025-towards
%X Large language models (LLMs) hold promise for therapeutic interventions, yet most existing datasets rely solely on text, overlooking non-verbal emotional cues essential to real-world therapy. To address this, we introduce a multimodal dataset of 1,441 publicly sourced therapy session videos containing both dialogue and non-verbal signals such as facial expressions and vocal tone. Inspired by Hochschild’s concept of emotional labor, we propose a computational formulation of emotional dissonance—the mismatch between facial and vocal emotion—and use it to guide emotionally aware prompting. Our experiments show that integrating multimodal cues, especially dissonance, improves the quality of generated interventions. We also find that LLM-based evaluators misalign with expert assessments in this domain, highlighting the need for human-centered evaluation. Data and code will be released to support future research.
%U https://aclanthology.org/2025.emnlp-main.1664/
%P 32724-32743
Markdown (Informal)
[Towards AI-Assisted Psychotherapy: Emotion-Guided Generative Interventions](https://aclanthology.org/2025.emnlp-main.1664/) (Haydarov et al., EMNLP 2025)
ACL