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.
The National Study of Mental Health and Wellbeing provides key statistics on mental health issues in Australia, including the prevalence of mental disorders, consultations with health professionals, and the use of mental health-related medications. The study covers a wide range of mental health conditions and offers insights into the impact of mental health on individuals and society.
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.
Psychology Wiki Datasetpsychology_wiki数据集的构建基于心理学领域的英文维基百科内容,通过系统化的数据采集与整理,确保了信息的广泛覆盖与深度挖掘。数据集中的每一篇文章均经过严格的筛选与标注,涵盖了标题、正文、相关性、受欢迎程度及排名等多个维度,为心理学研究提供了丰富的文本资源。