mlsanityData to 7 Oct 2026

Challenges of Large Language Models for Mental Health Counseling

arXiv 2311.13857 ↗First posted 23 Nov 2023cs.CL cs.AI cs.HCcited by 16

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Abstract (arXiv)

The global mental health crisis is looming with a rapid increase in mental disorders, limited resources, and the social stigma of seeking treatment. As the field of artificial intelligence (AI) has witnessed significant advancements in recent years, large language models (LLMs) capable of understanding and generating human-like text may be used in supporting or providing psychological counseling. However, the application of LLMs in the mental health domain raises concerns regarding the accuracy, effectiveness, and reliability of the information provided. This paper investigates the major challenges associated with the development of LLMs for psychological counseling, including model hallucination, interpretability, bias, privacy, and clinical effectiveness. We explore potential solutions to these challenges that are practical and applicable to the current paradigm of AI. From our experience in developing and deploying LLMs for mental health, AI holds a great promise for improving mental health care, if we can carefully navigate and overcome pitfalls of LLMs.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 23 of its 67 references that are in the map. First posted on arXiv 23 Nov 2023.

Builds on

23 of 67 references.

References · 23

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 9; 2026: 5.

16 papers.

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  1. 2024Towards Trustworthy AI: A Review of Ethical and Robust Large Language Modelscited by 21
  2. 2025Preference Learning Unlocks LLMs' Psycho-Counseling Skillscited by 9
  3. 2025TeleRAG: Efficient Retrieval-Augmented Generation Inference with Lookahead Retrievalcited by 9
  4. 2025Trustworthy AI Psychotherapy: Multi-Agent LLM Workflow for Counseling and Explainable Mental Disorder Diagnosiscited by 8
  5. 2025Between Help and Harm: An Evaluation of Mental Health Crisis Handling by LLMscited by 6
  6. 2025FedMentor: Domain-Aware Differential Privacy for Heterogeneous Federated LLMs in Mental Healthcited by 5
  7. 2024Enhancing Psychotherapy Counseling: A Data Augmentation Pipeline Leveraging Large Language Models for Counseling Conversationscited by 3
  8. 2025Love, Lies, and Language Models: Investigating AI's Role in Romance-Baiting Scamscited by 3
  9. 2025Evaluating Large Language Models for Anxiety, Depression, and Stress Detection: Insights into Prompting Strategies and Synthetic Datacited by 2
  10. 2025Hacia la interpretabilidad de la detecci'on anticipada de riesgos de depresi'on utilizando grandes modelos de lenguajecited by 1
  11. 2025AI-Augmented LLMs Achieve Therapist-Level Responses in Motivational Interviewingcited by 1
  12. 2026EFT-CoT: A Multi-Agent Chain-of-Thought Framework for Emotion-Focused Therapycited by 1
4 more
  1. 2026Initial Risk Probing and Feasibility Testing of Glow: a Generative AI-Powered Dialectical Behavior Therapy Skills Coach for Substance Use Recovery and HIV Preventioncited by 0
  2. 2026Alignment Plausibility: A New Standard for Assuring AI in Healthcarecited by 0
  3. 2026Candidate Generation and Definition-Guided Verification for Sentence-Level Depression Symptom Recognitioncited by 0
  4. 2026BiGraph-Diffuse: A Bidirectional Diffusion Language Model with Graph-Structured Retrieval For Mental Health Counselingcited by 0