# Thousand Voices of Trauma: A Large-Scale Synthetic Dataset for Modeling Prolonged Exposure Therapy Conversations

arXiv: https://arxiv.org/abs/2504.13955 · HTML: https://mlsanity.com/paper/2504.13955/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 124 references that are in the map.
- First posted on arXiv 16 Apr 2025.

## Builds on (most cited first, 6 of 6)

- [Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models](https://mlsanity.com/paper/2311.00287/) (2023)
- [Data Generation Using Large Language Models for Text Classification: An Empirical Case Study](https://mlsanity.com/paper/2407.12813/) (2024)
- [Privacy Sensitive Speech Analysis Using Federated Learning to Assess Depression](https://mlsanity.com/paper/2205.00111/) (2022)
- [Towards Algorithmic Fidelity: Mental Health Representation across Demographics in Synthetic vs. Human-generated Data](https://mlsanity.com/paper/2403.16909/) (2024)
- [Robust Privacy Amidst Innovation with Large Language Models Through a Critical Assessment of the Risks](https://mlsanity.com/paper/2407.16166/) (2024)
- [Improving Clinical NLP Performance through Language Model-Generated Synthetic Clinical Data](https://mlsanity.com/paper/2403.19511/) (2024)

## Built on by (most cited first, 11 of 11)

- [How Real Are Synthetic Therapy Conversations? Evaluating Fidelity in Prolonged Exposure Dialogues](https://mlsanity.com/paper/2504.21800/) (2025)
- [A Tale of Two Identities: An Ethical Audit of Human and AI-Crafted Personas](https://mlsanity.com/paper/2505.07850/) (2025)
- [The Pursuit of Empathy: Evaluating Small Language Models for PTSD Dialogue Support](https://mlsanity.com/paper/2505.15065/) (2025)
- [MAGneT: Coordinated Multi-Agent Generation of Synthetic Multi-Turn Mental Health Counseling Sessions](https://mlsanity.com/paper/2509.04183/) (2025)
- [Fine-Tuning Large Audio-Language Models with LoRA for Precise Temporal Localization of Prolonged Exposure Therapy Elements](https://mlsanity.com/paper/2506.09707/) (2025)
- [A Comprehensive Review of Datasets for Clinical Mental Health AI Systems](https://mlsanity.com/paper/2508.09809/) (2025)
- [Measuring What Matters!! Assessing Therapeutic Principles in Mental-Health Conversation](https://mlsanity.com/paper/2604.05795/) (2026)
- [AI Safety Training Can be Clinically Harmful](https://mlsanity.com/paper/2604.23445/) (2026)
- [CBT-Audio: Evaluating Audio Language Models for Patient-Side Distress Intensity Estimation in CBT Session Recordings](https://mlsanity.com/paper/2605.17370/) (2026)
- [EmoTrack: Clinical-Semantic Modeling for Text-Based Depression Severity Estimation](https://mlsanity.com/paper/2605.22286/) (2026)
- [LongCounsel-8: A Benchmark Suite for Longitudinal Depression Tracking from Multi-Session Counseling Dialogues](https://mlsanity.com/paper/2609.03507/) (2026)

## Abstract (arXiv)

The advancement of AI systems for mental health support is hindered by limited access to therapeutic conversation data, particularly for trauma treatment. We present Thousand Voices of Trauma, a synthetic benchmark dataset of 3,000 therapy conversations based on Prolonged Exposure therapy protocols for Post-traumatic Stress Disorder (PTSD). The dataset comprises 500 unique cases, each explored through six conversational perspectives that mirror the progression of therapy from initial anxiety to peak distress to emotional processing. We incorporated diverse demographic profiles (ages 18-80, M=49.3, 49.4% male, 44.4% female, 6.2% non-binary), 20 trauma types, and 10 trauma-related behaviors using deterministic and probabilistic generation methods. Analysis reveals realistic distributions of trauma types (witnessing violence 10.6%, bullying 10.2%) and symptoms (nightmares 23.4%, substance abuse 20.8%). Clinical experts validated the dataset's therapeutic fidelity, highlighting its emotional depth while suggesting refinements for greater authenticity. We also developed an emotional trajectory benchmark with standardized metrics for evaluating model responses. This privacy-preserving dataset addresses critical gaps in trauma-focused mental health data, offering a valuable resource for advancing both patient-facing applications and clinician training tools.