Finding Your Voice: The Linguistic Development of Mental Health Counselors (P19-1)
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| Challenge: | a longitudinal study of mental health counseling shows that counselors change their conversational behavior to become more diverse across interactions. |
| Approach: | They propose a computational framework to quantify the extent to which individuals change their linguistic behavior with experience. |
| Outcome: | The proposed framework quantifies the extent to which individuals change their linguistic behavior with experience and examines the nature of this evolution. |
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| Challenge: | Behavioral and mental health disorders are the most costly and prevalent conditions worldwide. |
| Approach: | They propose to use a dataset to analyze counseling interactions by using aspects such as mirroring, empathy, and reflective listening to build text-based classifiers. |
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| Challenge: | Recent surge of text-based online counseling applications enables us to collect and analyze interactions between counselors and clients. |
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A Survey of Large Language Models in Psychotherapy: Current Landscape and Future Directions (2025.findings-acl)
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Hongbin Na, Yining Hua, Zimu Wang, Tao Shen, Beibei Yu, Lilin Wang, Wei Wang, John Torous, Ling Chen
| Challenge: | Large language models (LLMs) can handle extensive context and multi-turn reasoning. |
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What Makes a Good Counselor? Learning to Distinguish between High-quality and Low-quality Counseling Conversations (P19-1)
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| Challenge: | Qualitative counseling relies on active collaboration between clients and counselors . |
| Approach: | They propose to use linguistic features to capture differences between high- and low-quality counseling conversations to build automatic classifiers that can predict counseling quality with accuracies of up to 88%. |
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| Challenge: | linguistic complexity loss in text-based therapy can be used to identify patterns of mental health . authors: clients who reported more anxiety used less lexically diverse language . |
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| Challenge: | Cultural and language factors influence counseling, but research has not explored whether this applies to other languages. |
| Approach: | They introduce a Spanish-language counseling dataset that contains expert annotations for counseling reflections and questions. |
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Do Large Language Models Align with Core Mental Health Counseling Competencies? (2025.findings-naacl)
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Viet Cuong Nguyen, Mohammad Taher, Dongwan Hong, Vinicius Konkolics Possobom, Vibha Thirunellayi Gopalakrishnan, Ekta Raj, Zihang Li, Heather J. Soled, Michael L. Birnbaum, Srijan Kumar, Munmun De Choudhury
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Allison Lahnala, Yuntian Zhao, Charles Welch, Jonathan K. Kummerfeld, Lawrence C An, Kenneth Resnicow, Rada Mihalcea, Verónica Pérez-Rosas
| Challenge: | Increasing number of people engage in online health forums, making it important to understand the quality of the advice they receive. |
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Language and Mental Health: Measures of Emotion Dynamics from Text as Linguistic Biosocial Markers (2023.emnlp-main)
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| Challenge: | valence variability was significantly lower in the control group compared to ADHD, depression, bipolar disorder, MDD, PTSD, and OCD but not PPD. |
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Towards Understanding Counseling Conversations: Domain Knowledge and Large Language Models (2024.findings-eacl)
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| Challenge: | Existing language models such as Transformer-based models fail to predict the conversation outcome. |
| Approach: | They propose to integrate human-annotated domain knowledge and LLM-generated features to provide richer context to counseling conversations. |
| Outcome: | The proposed model improves by 15% when combined with human-annotated domain knowledge and LLM-generated features. |