This paper discusses Helply - a synthesized ML training dataset focused on psychology and therapy, created by Alex Scott and published by NamelessAI. The dataset developed by Alex Scott is a comprehensive collection of synthesized data designed to train LLMs in understanding psychological and therapeutic contexts. This dataset aims to simulate real-world interactions between therapists and patients, enabling ML models to learn from a wide range of scenarios and therapeutic techniques.
The Helply dataset is a comprehensive synthetic ML training dataset created by Alex Scott and released by NamelessAI, focusing on the fields of psychology and therapy. The dataset is designed to train large language models (LLMs) to understand and simulate human psychological processes. By combining existing psychology literature, therapy session records, and patient self-report data, the Helply dataset covers a variety of treatment scenarios, such as cognitive behavioral therapy (CBT), internal family systems (IFS), and internet-based cognitive behavioral therapy (iCBT). In addition, the dataset emphasizes the dynamic interaction between patients and therapists, capturing communication details that affect treatment outcomes. Despite challenges such as ethical considerations and model generalization, the Helply dataset has revolutionary potential to change the understanding and application of therapeutic practices in digital environments.
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 Lothian Diary Project consists of 125+ audio/video recordings collected from residents of Edinburgh and the Lothian counties in Scotland. Participants discuss their experiences during different stages of the Covid-19 pandemic. The recordings are accompanied by transcriptions and demographic information.
The IC-AnnoMI repository contains source code and a synthetic dataset generated through in-context zero-shot LLM prompting for mental health and therapeutic counselling. IC-AnnoMI is a project that generates contextual MI dialogues using large language models (LLMs). The project contains source code and a synthetic dataset generated through zero-shot prompts, aiming to address the data scarcity and inherent bias problems in mental health and therapeutic consultation.