# SSR: Can Simulated Patients Learn to Stigmatize Themselves? Modeling Self-Stigma through Internal Monologue

arXiv: https://arxiv.org/abs/2606.08254 · HTML: https://mlsanity.com/paper/2606.08254/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 6 Jun 2026.

## Builds on (most cited first, 9 of 9)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Towards Emotional Support Dialog Systems](https://mlsanity.com/paper/2106.01144/) (2021)
- [D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat](https://mlsanity.com/paper/2205.11764/) (2022)
- [Symptom Identification for Interpretable Detection of Multiple Mental Disorders](https://mlsanity.com/paper/2205.11308/) (2022)
- [Depression Diagnosis Dialogue Simulation: Self-improving Psychiatrist with Tertiary Memory](https://mlsanity.com/paper/2409.15084/) (2024)
- [Trustworthy AI Psychotherapy: Multi-Agent LLM Workflow for Counseling and Explainable Mental Disorder Diagnosis](https://mlsanity.com/paper/2508.11398/) (2025)
- [What is Stigma Attributed to? A Theory-Grounded, Expert-Annotated Interview Corpus for Demystifying Mental-Health Stigma](https://mlsanity.com/paper/2505.12727/) (2025)
- [Breaking the Stigma! Unobtrusively Probe Symptoms in Depression Disorder Diagnosis Dialogue](https://mlsanity.com/paper/2501.15260/) (2025)

## Abstract (arXiv)

Simulating patients with large language models (LLMs) is a promising tool for mental health training, but existing approaches fail to capture a key clinical reality: self-stigma. Patients experiencing self-stigma, the internalization of negative stereotypes, often exhibit context-sensitive resistance, such as avoidance, denial, or self-blame, which current models render as static or uniformly compliant behavior. To address this, we introduce a novel simulation framework grounded in the psychological 3A1H model of self-stigmatization. Our core innovation is the creation of a Stigmatized Self-Reflection (SSR) dataset, where we augment mental health dialogues with internal monologues that reflect stigma-aware reasoning. By fine-tuning LLMs with this data using a chain-of-thought approach, we train patient agents to dynamically adjust their level and expression of stigma based on conversational triggers. Evaluations demonstrate that our approach significantly outperforms specialized baselines, generating more authentic and situationally appropriate patient responses. This work provides a crucial step towards realistic stigma simulation for clinical training and empathetic dialogue systems.