Social Aware Synthetic Data Generation For Sucidal Idention Detection Using Large Language Models

Suicidal ideation detection is a vital research area that holds great potential for improving mental health support systems.However, the sensitivity surrounding suicide-related data poses challenges in accessing large-scale, annotated datasets necessary for training effective machine learning models.To address this limitation, we introduce an innovative strategy that leverages the capabilities of generative AI models, such as ChatGPT, Flan-T5, and Llama, to create synthetic data for suicidal ideation detection.Our data generation approach is grounded in social factors extracted from psychology literature and aims to ensure coverage of essential information related to suicidal ideation.In our study, we benchmarked against state-of-the-art NLP classification models, specifically, those centered around the BERT family structures.When trained on the real-world dataset, UMD, these conventional models tend to yield F1-scores ranging.