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 t4d project is a direct implementation of the conversion algorithm from the ToMi dataset to the T4D dataset. It is designed to filter and process examples that involve Theory of Mind questions, providing a valuable resource for researchers working on cognitive and social AI models. The project is built to convert a predefined dataset A (ToMi) to dataset B (T4D) and is licensed under the Apache License, Version 2.0.
The Weibo User Depression Detection Dataset is a large-scale dataset for detecting depression in Weibo users. It includes user profiles, tweets, and labels indicating whether the user is depressed. The dataset is useful for researchers working on mental health and social media analysis.
Lingxin (SoulChat) is a psychological health large model fine-tuned with millions of Chinese long-text instructions and multi-turn empathetic dialogue data in the field of psychological counseling.
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.