# Rethinking Large Language Models in Mental Health Applications

arXiv: https://arxiv.org/abs/2311.11267 · HTML: https://mlsanity.com/paper/2311.11267/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 27 of its 49 references that are in the map.
- First posted on arXiv 19 Nov 2023.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 27 of 27)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting](https://mlsanity.com/paper/2305.04388/) (2023)
- [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://mlsanity.com/paper/2302.00093/) (2023)
- [Transformers learn in-context by gradient descent](https://mlsanity.com/paper/2212.07677/) (2022)
- [Explainability for Large Language Models: A Survey](https://mlsanity.com/paper/2309.01029/) (2023)
- [MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare](https://mlsanity.com/paper/2110.15621/) (2021)
- [Auditing large language models: a three-layered approach](https://mlsanity.com/paper/2302.08500/) (2023)
- [Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach](https://mlsanity.com/paper/2101.07714/) (2021)
- [Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data](https://mlsanity.com/paper/2307.14385/) (2023)
- [Towards Interpretable Mental Health Analysis with Large Language Models](https://mlsanity.com/paper/2304.03347/) (2023)
- [SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Support](https://mlsanity.com/paper/2305.00450/) (2023)
- [ChatCounselor: A Large Language Models for Mental Health Support](https://mlsanity.com/paper/2309.15461/) (2023)
- [On the State of Social Media Data for Mental Health Research](https://mlsanity.com/paper/2011.05233/) (2020)
- [Interactive Natural Language Processing](https://mlsanity.com/paper/2305.13246/) (2023)
- [Domain-specific Continued Pretraining of Language Models for Capturing Long Context in Mental Health](https://mlsanity.com/paper/2304.10447/) (2023)
- [Enhancing Psychological Counseling with Large Language Model: A Multifaceted Decision-Support System for Non-Professionals](https://mlsanity.com/paper/2308.15192/) (2023)
- [Towards an Online Empathetic Chatbot with Emotion Causes](https://mlsanity.com/paper/2105.11903/) (2021)
- [A Benchmark for Understanding Dialogue Safety in Mental Health Support](https://mlsanity.com/paper/2307.16457/) (2023)
- [LCA: Loss Change Allocation for Neural Network Training](https://mlsanity.com/paper/1909.01440/) (2019)
- [EmpHi: Generating Empathetic Responses with Human-like Intents](https://mlsanity.com/paper/2204.12191/) (2022)
- [Understanding Client Reactions in Online Mental Health Counseling](https://mlsanity.com/paper/2306.15334/) (2023)
- [Benchmarking for Public Health Surveillance tasks on Social Media with a Domain-Specific Pretrained Language Model](https://mlsanity.com/paper/2204.04521/) (2022)
- [A Meta-Learning Perspective on Transformers for Causal Language Modeling](https://mlsanity.com/paper/2310.05884/) (2023)

## Built on by (most cited first, 16 of 16)

- [Can Large Language Models be Good Emotional Supporter? Mitigating Preference Bias on Emotional Support Conversation](https://mlsanity.com/paper/2402.13211/) (2024)
- [Large Language Models in Mental Health Care: a Scoping Review](https://mlsanity.com/paper/2401.02984/) (2024)
- [Understanding the Therapeutic Relationship between Counselors and Clients in Online Text-based Counseling using LLMs](https://mlsanity.com/paper/2402.11958/) (2024)
- [When Can We Trust LLMs in Mental Health? Large-Scale Benchmarks for Reliable LLM Evaluation](https://mlsanity.com/paper/2510.19032/) (2025)
- [Can Large Language Models Identify Implicit Suicidal Ideation? An Empirical Evaluation](https://mlsanity.com/paper/2502.17899/) (2025)
- [CBT-Bench: Evaluating Large Language Models on Assisting Cognitive Behavior Therapy](https://mlsanity.com/paper/2410.13218/) (2024)
- [Can You Share Your Story? Modeling Clients' Metacognition and Openness for LLM Therapist Evaluation](https://mlsanity.com/paper/2507.19643/) (2025)
- [Harnessing Large Language Models Over Transformer Models for Detecting Bengali Depressive Social Media Text: A Comprehensive Study](https://mlsanity.com/paper/2401.07310/) (2024)
- [Zero-shot Explainable Mental Health Analysis on Social Media by Incorporating Mental Scales](https://mlsanity.com/paper/2402.10948/) (2024)
- [Toward Clinically Explainable AI for Medical Diagnosis: A Foundation Model with Human-Compatible Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2509.03906/) (2025)
- [Automating PTSD Diagnostics in Clinical Interviews: Leveraging Large Language Models for Trauma Assessments](https://mlsanity.com/paper/2405.11178/) (2024)
- [Can LLMs Address Mental Health Questions? A Comparison with Human Therapists](https://mlsanity.com/paper/2509.12102/) (2025)
- [Keyphrase Generative Representation of Youth Crisis Conversations Beyond Static Taxonomies](https://mlsanity.com/paper/2605.27546/) (2026)
- [CARE: An Explainable Computational Framework for Assessing Client-Perceived Therapeutic Alliance Using Large Language Models](https://mlsanity.com/paper/2602.20648/) (2026)
- [Training Therapeutic Judges and Multi-Agent Systems for Human-Aligned Mental Health Support](https://mlsanity.com/paper/2606.30887/) (2026)
- [BiGraph-Diffuse: A Bidirectional Diffusion Language Model with Graph-Structured Retrieval For Mental Health Counseling](https://mlsanity.com/paper/2609.29519/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have become valuable assets in mental health, showing promise in both classification tasks and counseling applications. This paper offers a perspective on using LLMs in mental health applications. It discusses the instability of generative models for prediction and the potential for generating hallucinatory outputs, underscoring the need for ongoing audits and evaluations to maintain their reliability and dependability. The paper also distinguishes between the often interchangeable terms ``explainability'' and ``interpretability'', advocating for developing inherently interpretable methods instead of relying on potentially hallucinated self-explanations generated by LLMs. Despite the advancements in LLMs, human counselors' empathetic understanding, nuanced interpretation, and contextual awareness remain irreplaceable in the sensitive and complex realm of mental health counseling. The use of LLMs should be approached with a judicious and considerate mindset, viewing them as tools that complement human expertise rather than seeking to replace it.