@inproceedings{xia-etal-2026-psychechat,
title = "{P}sych{\={e}}{C}hat: An Empathic Framework Focused on Emotion Shift Tracking and Safety Risk Analysis in Psychological Counseling",
author = "Xia, Zhentao and
Fan, Yongqi and
Chu, Yuxiang and
Yin, Yichao and
Chen, Liangliang and
Ruan, Tong and
Zhang, Weiyan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1133/",
pages = "25953--25985",
ISBN = "979-8-89176-459-0",
abstract = "Large language models (LLMs) have demonstrated notable advancements in psychological counseling. However, existing models generally do not explicitly model seekers' emotion shifts across counseling sessions, a core focus in classical psychological schools. Moreover, how to align counselor models' responses with these emotion shifts while proactively mitigating safety risks remains underexplored. To bridge these gaps, we propose Psych{\={e}}Chat, which explicitly integrates emotion shift tracking and safety risk analysis for psychological counseling. Specifically, we employ interactive role-playing to synthesize counselor-seeker dialogues, incorporating two modules: Emotion Management Module, to capture seekers' current emotions and emotion shifts; and Risk Control Module, to anticipate seekers' subsequent reactions and identify potential risks. Furthermore, we introduce two modeling paradigms. The Agent Mode structures emotion management, risk control, and counselor responses into a collaborative multi-agent pipeline. The LLM Mode integrates these stages into a unified chain-of-thought for end-to-end inference, balancing efficiency and performance. Experiments with simulated interaction, dialogue-level evaluation, ablation studies, and expert assessment indicate that Psych{\={e}}Chat improves emotion-shift awareness and risk-aware response generation in psychological counseling support. Our data and code are available at https://github.com/XNeil12138/PsycheChat."
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<abstract>Large language models (LLMs) have demonstrated notable advancements in psychological counseling. However, existing models generally do not explicitly model seekers’ emotion shifts across counseling sessions, a core focus in classical psychological schools. Moreover, how to align counselor models’ responses with these emotion shifts while proactively mitigating safety risks remains underexplored. To bridge these gaps, we propose PsychēChat, which explicitly integrates emotion shift tracking and safety risk analysis for psychological counseling. Specifically, we employ interactive role-playing to synthesize counselor-seeker dialogues, incorporating two modules: Emotion Management Module, to capture seekers’ current emotions and emotion shifts; and Risk Control Module, to anticipate seekers’ subsequent reactions and identify potential risks. Furthermore, we introduce two modeling paradigms. The Agent Mode structures emotion management, risk control, and counselor responses into a collaborative multi-agent pipeline. The LLM Mode integrates these stages into a unified chain-of-thought for end-to-end inference, balancing efficiency and performance. Experiments with simulated interaction, dialogue-level evaluation, ablation studies, and expert assessment indicate that PsychēChat improves emotion-shift awareness and risk-aware response generation in psychological counseling support. Our data and code are available at https://github.com/XNeil12138/PsycheChat.</abstract>
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%0 Conference Proceedings
%T PsychēChat: An Empathic Framework Focused on Emotion Shift Tracking and Safety Risk Analysis in Psychological Counseling
%A Xia, Zhentao
%A Fan, Yongqi
%A Chu, Yuxiang
%A Yin, Yichao
%A Chen, Liangliang
%A Ruan, Tong
%A Zhang, Weiyan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F xia-etal-2026-psychechat
%X Large language models (LLMs) have demonstrated notable advancements in psychological counseling. However, existing models generally do not explicitly model seekers’ emotion shifts across counseling sessions, a core focus in classical psychological schools. Moreover, how to align counselor models’ responses with these emotion shifts while proactively mitigating safety risks remains underexplored. To bridge these gaps, we propose PsychēChat, which explicitly integrates emotion shift tracking and safety risk analysis for psychological counseling. Specifically, we employ interactive role-playing to synthesize counselor-seeker dialogues, incorporating two modules: Emotion Management Module, to capture seekers’ current emotions and emotion shifts; and Risk Control Module, to anticipate seekers’ subsequent reactions and identify potential risks. Furthermore, we introduce two modeling paradigms. The Agent Mode structures emotion management, risk control, and counselor responses into a collaborative multi-agent pipeline. The LLM Mode integrates these stages into a unified chain-of-thought for end-to-end inference, balancing efficiency and performance. Experiments with simulated interaction, dialogue-level evaluation, ablation studies, and expert assessment indicate that PsychēChat improves emotion-shift awareness and risk-aware response generation in psychological counseling support. Our data and code are available at https://github.com/XNeil12138/PsycheChat.
%U https://aclanthology.org/2026.emnlp-main.1133/
%P 25953-25985
Markdown (Informal)
[PsychēChat: An Empathic Framework Focused on Emotion Shift Tracking and Safety Risk Analysis in Psychological Counseling](https://aclanthology.org/2026.emnlp-main.1133/) (Xia et al., EMNLP 2026)
ACL