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.
IC-AnnoMI is an official repository that employs Large Language Models (LLMs) to generate in-context Motivational Interviewing (MI) dialogues. The repository includes a dataset folder with annotated MI dialogues across psychological and linguistic dimensions. It also provides a test set for experiments. The project aims to address scarce data and inherent bias challenges in mental health and therapeutic counselling by leveraging the capabilities of LLMs. The IC-AnnoMI project generates contextual MI dialogues through large language models and provides a synthetic dataset for training and testing MI dialogue systems. The project contains detailed annotation files covering dialogue annotations in psychological and linguistic dimensions, suitable for research in mental health and therapeutic consultation.
The Mental Health Corpus contains labeled comments on mental health issues, used for sentiment and toxic language analysis.
Psy-Insight is a bilingual, interpretable multi-turn dataset for mental health counseling dialogues. It includes 6,208 rounds of multi-turn counseling dialogues in English and 5,776 rounds in Chinese, annotated with step-by-step reasoning labels and multi-task labels. This dataset is designed to support the application of large language models in mental health and is suitable for tasks such as emotion classification and psychological treatment interpretation.
The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) uses epidemiological, behavioral, and neuroimaging data to understand how individuals can best retain cognitive abilities into old age. The Cam-CAN Data Access Portal provides access to datasets from the Cambridge Centre for Ageing and Neuroscience, including neuroimaging and cognitive data from participants aged 18-90.