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.
This dataset contains survey responses from individuals in the tech industry about their mental health, including questions about treatment, workplace resources, and attitudes towards discussing mental health in the workplace. By analyzing this dataset, we can better understand how prevalent mental health issues are among those who work in the tech sector—and what kinds of resources they rely upon to find help—so that more can be done to create a healthier working environment for all.
SoulChat2.0 is a framework for constructing the digital twin of psychological counselors, designed to support the development of AI applications in mental health. It includes a data generation module and a modeling module, enabling the creation of personalized counseling models based on limited real-world counseling cases.
The DS4C dataset is a structured collection of COVID-19 data from South Korea, based on reports from the Korea Centers for Disease Control & Prevention (KCDC) and local governments. It includes information on infections, patient routes, and various analyses. The dataset has been used for multiple research and visualization projects.