面向心理健康的高效穩健語言情感診斷：基於多智能體指令優化方法

Towards Efficient and Robust Linguistic Emotion Diagnosis for Mental Health via Multi-Agent Instruction Refinement

January 20, 2026

作者: Jian Zhang, Zhangqi Wang, Zhiyuan Wang, Weiping Fu, Yu He, Haiping Zhu, Qika Lin, Jun Liu

cs.AI

摘要

在臨床病歷、心理諮詢對話及線上心理健康社群中，抑鬱、焦慮及創傷相關狀態的情感語言表達無處不在，準確識別這些情緒對於臨床分診、風險評估和及時干預至關重要。儘管大型語言模型在情感分析任務中展現出強大的泛化能力，但其在高風險、情境複雜的醫療環境中的診斷可靠性仍高度依賴提示設計。現有方法面臨兩大關鍵挑戰：情感共病現象（多種交織情感狀態使預測複雜化）以及臨床線索探索效率不足。為解決這些難題，我們提出APOLO框架（面向語言情感診斷的自動化提示優化），通過系統性探索更廣闊且更細粒度的提示空間來提升診斷效率與穩健性。APOLO將指令優化建模為部分可觀測馬爾可夫決策過程，採用規劃者、教師、評判者、學生與目標代理的多智能體協作機制。在此閉環框架中，規劃者定義優化路徑，教師-評判者-學生代理迭代精煉提示以增強推理穩定性與有效性，目標代理則根據性能評估決定是否繼續優化。實驗結果表明，APOLO在領域專用與分層基準測試中持續提升診斷準確度與穩健性，為心理健康領域構建可信賴的大型語言模型應用提供了可擴展、可泛化的範式。

English

Linguistic expressions of emotions such as depression, anxiety, and trauma-related states are pervasive in clinical notes, counseling dialogues, and online mental health communities, and accurate recognition of these emotions is essential for clinical triage, risk assessment, and timely intervention. Although large language models (LLMs) have demonstrated strong generalization ability in emotion analysis tasks, their diagnostic reliability in high-stakes, context-intensive medical settings remains highly sensitive to prompt design. Moreover, existing methods face two key challenges: emotional comorbidity, in which multiple intertwined emotional states complicate prediction, and inefficient exploration of clinically relevant cues. To address these challenges, we propose APOLO (Automated Prompt Optimization for Linguistic Emotion Diagnosis), a framework that systematically explores a broader and finer-grained prompt space to improve diagnostic efficiency and robustness. APOLO formulates instruction refinement as a Partially Observable Markov Decision Process and adopts a multi-agent collaboration mechanism involving Planner, Teacher, Critic, Student, and Target roles. Within this closed-loop framework, the Planner defines an optimization trajectory, while the Teacher-Critic-Student agents iteratively refine prompts to enhance reasoning stability and effectiveness, and the Target agent determines whether to continue optimization based on performance evaluation. Experimental results show that APOLO consistently improves diagnostic accuracy and robustness across domain-specific and stratified benchmarks, demonstrating a scalable and generalizable paradigm for trustworthy LLM applications in mental healthcare.