Every veteran knows and has had a 'Gunny': Semper Fidelis. This dataset is designed for conversational AI systems to assist veterans from various military branches, including U.S. and U.K. armed forces.
Every veteran knows and has had a 'Gunny': Semper Fidelis. This dataset is designed for conversational AI systems to assist veterans from various military branches, including U.S. and U.K. armed forces. The dataset uses multiple personas from different branches (9) to be exact, each dedicated to providing support for veterans dealing with PTSD and transitioning to civilian life. The personas offer advice rooted in discipline, accountability, and mental resilience, while maintaining the appropriate tone and ethos of each military branch. Each persona emphasizes the importance of seeking professional help when necessary, without substituting for therapy, but there is no guarentee. All data was generated using Meta's - Llama-3.2-3B-Instruct.
This project implements the conversion algorithm from the ToMi dataset to the T4D (Thinking is for Doing) dataset, as introduced in the paper https://arxiv.org/abs/2310.03051. It filters examples with Theory of Mind (ToM) questions and adapts the algorithm to account for second-order false beliefs.
The Substance Abuse and Mental Health Data Archive (SAMHDA) provides a comprehensive collection of data sets related to mental health and substance use. It includes ongoing studies, population surveys, treatment facility surveys, and client-level data, offering valuable insights for researchers and policymakers.
The data is originally source from (Sun et al,2021). (Liu et al, 2023) processed the data to make it a dataset vis huggingface api with taining/validation/testing splitting