@inproceedings{chedalla-etal-2025-turn,
title = "Turn-by-Turn Behavior Monitoring in {LM}-Guided Psychotherapy",
author = "Chedalla, Anish Sai and
Ali, Samina and
Chen, Jiuming and
Starborn0128@gmail.com, Starborn0128@gmail.com and
Xia, Eric",
editor = "T.y.s.s, Santosh and
Shimizu, Shuichiro and
Gong, Yifan",
booktitle = "The 14th International Joint Conference on Natural Language Processing and The 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-srw.10/",
pages = "105--122",
ISBN = "979-8-89176-304-3",
abstract = "Large language models (LLMs) have the potential to be powerful instruments for psychotherapy. However, there is a shortage of practical tools to support their use in production. We develop a novel, iterative process of updating conversational context for tracking EIS (Emotional Intelligence Scale) instantaneously, and test Llama-70b. Through this, we show that (1) EIS varies more on psychotherapeutic (emotional support) conversations than control (emotionally unstimulating) conversations and (2) model responses can be systematically classified to identify consistent patterns. Thus, EIS is a valid indicator of empathetic model behavior. Rises in the EIS score correspond to prosocial behavior, and falls correspond to detached, unsocial behavior. These results suggest that psychometric questionnaires like EIS can provide a structured lens for observing empathetic stability of models and offer a foundation for future work on their role in psychotherapy."
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%0 Conference Proceedings
%T Turn-by-Turn Behavior Monitoring in LM-Guided Psychotherapy
%A Chedalla, Anish Sai
%A Ali, Samina
%A Chen, Jiuming
%A Starborn0128@gmail.com, Starborn0128@gmail.com
%A Xia, Eric
%Y T.y.s.s, Santosh
%Y Shimizu, Shuichiro
%Y Gong, Yifan
%S The 14th International Joint Conference on Natural Language Processing and The 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-304-3
%F chedalla-etal-2025-turn
%X Large language models (LLMs) have the potential to be powerful instruments for psychotherapy. However, there is a shortage of practical tools to support their use in production. We develop a novel, iterative process of updating conversational context for tracking EIS (Emotional Intelligence Scale) instantaneously, and test Llama-70b. Through this, we show that (1) EIS varies more on psychotherapeutic (emotional support) conversations than control (emotionally unstimulating) conversations and (2) model responses can be systematically classified to identify consistent patterns. Thus, EIS is a valid indicator of empathetic model behavior. Rises in the EIS score correspond to prosocial behavior, and falls correspond to detached, unsocial behavior. These results suggest that psychometric questionnaires like EIS can provide a structured lens for observing empathetic stability of models and offer a foundation for future work on their role in psychotherapy.
%U https://aclanthology.org/2025.ijcnlp-srw.10/
%P 105-122
Markdown (Informal)
[Turn-by-Turn Behavior Monitoring in LM-Guided Psychotherapy](https://aclanthology.org/2025.ijcnlp-srw.10/) (Chedalla et al., IJCNLP 2025)
ACL
- Anish Sai Chedalla, Samina Ali, Jiuming Chen, Starborn0128@gmail.com Starborn0128@gmail.com, and Eric Xia. 2025. Turn-by-Turn Behavior Monitoring in LM-Guided Psychotherapy. In The 14th International Joint Conference on Natural Language Processing and The 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 105–122, Mumbai, India. Association for Computational Linguistics.