DeepPsy-Agent: A Stage-Aware and Deep-Thinking Emotional Support Agent System

• URL: http://arxiv.org/abs/2503.15876v1
• Date: Thu, 20 Mar 2025 05:59:29 GMT
• Title: DeepPsy-Agent: A Stage-Aware and Deep-Thinking Emotional Support Agent System
• Authors: Kai Chen, Zebing Sun,
• Abstract summary: DeepPsy-Agent is an innovative psychological support system that combines the three-stage helping theory in psychology with deep learning techniques.<n>Based on 30,000 real psychological hotline conversations, we employ AI-simulated dialogues and expert re-annotation strategies to construct a high-quality multi-turn dialogue dataset.
• Score: 10.262822400879688
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces DeepPsy-Agent, an innovative psychological support system that combines the three-stage helping theory in psychology with deep learning techniques. The system consists of two core components: (1) a multi-stage response-capable dialogue model (\textit{deeppsy-chat}), which enhances reasoning capabilities through stage-awareness and deep-thinking analysis to generate high-quality responses; and (2) a real-time stage transition detection model that identifies contextual shifts to guide the dialogue towards more effective intervention stages. Based on 30,000 real psychological hotline conversations, we employ AI-simulated dialogues and expert re-annotation strategies to construct a high-quality multi-turn dialogue dataset. Experimental results demonstrate that DeepPsy-Agent outperforms general-purpose large language models (LLMs) in key metrics such as problem exposure completeness, cognitive restructuring success rate, and action adoption rate. Ablation studies further validate the effectiveness of stage-awareness and deep-thinking modules, showing that stage information contributes 42.3\% to performance, while the deep-thinking module increases root-cause identification by 58.3\% and reduces ineffective suggestions by 72.1\%. This system addresses critical challenges in AI-based psychological support through dynamic dialogue management and deep reasoning, advancing intelligent mental health services.

Related papers

• A Desideratum for Conversational Agents: Capabilities, Challenges, and Future Directions [51.96890647837277]
  Large Language Models (LLMs) have propelled conversational AI from traditional dialogue systems into sophisticated agents capable of autonomous actions, contextual awareness, and multi-turn interactions with users. This survey paper presents a desideratum for next-generation Conversational Agents - what has been achieved, what challenges persist, and what must be done for more scalable systems that approach human-level intelligence.
  arXiv  Detail & Related papers  (2025-04-07T21:01:25Z)
• Visual Agents as Fast and Slow Thinkers [88.1404921693082]
  We introduce FaST, which incorporates the Fast and Slow Thinking mechanism into visual agents.<n>FaST employs a switch adapter to dynamically select between System 1/2 modes.<n>It tackles uncertain and unseen objects by adjusting model confidence and integrating new contextual data.
  arXiv  Detail & Related papers  (2024-08-16T17:44:02Z)
• PsycoLLM: Enhancing LLM for Psychological Understanding and Evaluation [32.40846713004979]
  PsycoLLM is trained on a proposed high-quality psychological dataset.<n>We augment this process with real-world psychological case backgrounds extracted from online platforms.<n>We develop a comprehensive psychological benchmark based on authoritative psychological counseling examinations in China.
  arXiv  Detail & Related papers  (2024-07-08T08:25:56Z)
• Data Augmentation of Multi-turn Psychological Dialogue via Knowledge-driven Progressive Thought Prompting [46.919537239016734]
  Large language models (LLMs) have simplified the implementation of multi-turn dialogues. It remains challenging to deliver satisfactory performance in low-resource domain, like psychological dialogue dialogue. We propose a knowledge-driven progressive thought prompting method to guide LLM to generate psychology-related dialogue.
  arXiv  Detail & Related papers  (2024-06-24T12:02:56Z)
• Empathy Through Multimodality in Conversational Interfaces [1.360649555639909]
  Conversational Health Agents (CHAs) are redefining healthcare by offering nuanced support that transcends textual analysis to incorporate emotional intelligence. This paper introduces an LLM-based CHA engineered for rich, multimodal dialogue-especially in the realm of mental health support. It adeptly interprets and responds to users' emotional states by analyzing multimodal cues, thus delivering contextually aware and empathetically resonant verbal responses.
  arXiv  Detail & Related papers  (2024-05-08T02:48:29Z)
• The Future of Cognitive Strategy-enhanced Persuasive Dialogue Agents: New Perspectives and Trends [23.68567141617251]
  We present several fundamental cognitive psychology theories and give the formalized definition of three typical cognitive strategies. We propose a new system architecture by incorporating the formalized definition to lay the foundation of CogAgent.
  arXiv  Detail & Related papers  (2024-02-07T07:28:34Z)
• Empowering Psychotherapy with Large Language Models: Cognitive Distortion Detection through Diagnosis of Thought Prompting [82.64015366154884]
  We study the task of cognitive distortion detection and propose the Diagnosis of Thought (DoT) prompting. DoT performs diagnosis on the patient's speech via three stages: subjectivity assessment to separate the facts and the thoughts; contrastive reasoning to elicit the reasoning processes supporting and contradicting the thoughts; and schema analysis to summarize the cognition schemas. Experiments demonstrate that DoT obtains significant improvements over ChatGPT for cognitive distortion detection, while generating high-quality rationales approved by human experts.
  arXiv  Detail & Related papers  (2023-10-11T02:47:21Z)
• Enhancing Large Language Model Induced Task-Oriented Dialogue Systems Through Look-Forward Motivated Goals [76.69419538047813]
  ProToD approach anticipates the future dialogue actions and incorporates the goal-oriented reward signal to enhance ToD systems. We present a novel evaluation method that assesses ToD systems based on goal-driven dialogue simulations. Empirical experiments conducted on the MultiWoZ 2.1 dataset demonstrate that our model can achieve superior performance using only 10% of the data.
  arXiv  Detail & Related papers  (2023-09-16T10:56:00Z)
• A Hierarchical Regression Chain Framework for Affective Vocal Burst Recognition [72.36055502078193]
  We propose a hierarchical framework, based on chain regression models, for affective recognition from vocal bursts. To address the challenge of data sparsity, we also use self-supervised learning (SSL) representations with layer-wise and temporal aggregation modules. The proposed systems participated in the ACII Affective Vocal Burst (A-VB) Challenge 2022 and ranked first in the "TWO'' and "CULTURE" tasks.
  arXiv  Detail & Related papers  (2023-03-14T16:08:45Z)
• You Impress Me: Dialogue Generation via Mutual Persona Perception [62.89449096369027]
  The research in cognitive science suggests that understanding is an essential signal for a high-quality chit-chat conversation. Motivated by this, we propose P2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding.
  arXiv  Detail & Related papers  (2020-04-11T12:51:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.