# TalkDep: Clinically Grounded LLM Personas for Conversation-Centric Depression Screening

arXiv: https://arxiv.org/abs/2508.04248 · HTML: https://mlsanity.com/paper/2508.04248/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 6 Aug 2025.

## Builds on (most cited first, 5 of 5)

- [Gemma: Open Models Based on Gemini Research and Technology](https://mlsanity.com/paper/2403.08295/) (2024)
- [Roleplay-doh: Enabling Domain-Experts to Create LLM-simulated Patients via Eliciting and Adhering to Principles](https://mlsanity.com/paper/2407.00870/) (2024)
- [The opportunities and risks of large language models in mental health](https://mlsanity.com/paper/2403.14814/) (2024)
- [Using In-Context Learning to Improve Dialogue Safety](https://mlsanity.com/paper/2302.00871/) (2023)
- [Explainable Depression Symptom Detection in Social Media](https://mlsanity.com/paper/2310.13664/) (2023)

## Built on by (most cited first, 6 of 6)

- [PatientHub: A Unified Framework for Patient Simulation](https://mlsanity.com/paper/2602.11684/) (2026)
- [Assessing Risks of Large Language Models in Mental Health Support: A Framework for Automated Clinical AI Red Teaming](https://mlsanity.com/paper/2602.19948/) (2026)
- [Psychologically-Grounded Graph Modeling for Interpretable Depression Detection](https://mlsanity.com/paper/2604.24126/) (2026)
- [Creating Multilingual Mental Health Dialogue Datasets: Limits of Persona-Based Localization via Nationality and Language](https://mlsanity.com/paper/2606.19640/) (2026)
- [Self-Stigma Is Not a Monolith, but Generic Empathy Is: Persona-Conditioned LLM Support for People Who Use Drugs](https://mlsanity.com/paper/2606.23387/) (2026)
- [When Does Defendant Statement Matter? A Study of Bias and Persuasion in LLM-Simulated Jurors](https://mlsanity.com/paper/2609.09887/) (2026)

## Abstract (arXiv)

The increasing demand for mental health services has outpaced the availability of real training data to develop clinical professionals, leading to limited support for the diagnosis of depression. This shortage has motivated the development of simulated or virtual patients to assist in training and evaluation, but existing approaches often fail to generate clinically valid, natural, and diverse symptom presentations. In this work, we embrace the recent advanced language models as the backbone and propose a novel clinician-in-the-loop patient simulation pipeline, TalkDep, with access to diversified patient profiles to develop simulated patients. By conditioning the model on psychiatric diagnostic criteria, symptom severity scales, and contextual factors, our goal is to create authentic patient responses that can better support diagnostic model training and evaluation. We verify the reliability of these simulated patients with thorough assessments conducted by clinical professionals. The availability of validated simulated patients offers a scalable and adaptable resource for improving the robustness and generalisability of automatic depression diagnosis systems.