The ToM QA Dataset is designed to evaluate question-answering models' ability to reason about beliefs. It includes 3 task types and 4 question types, creating 12 total scenarios. The dataset is inspired by theory-of-mind experiments in developmental psychology and is used to test models' understanding of beliefs and inconsistent states of the world.
The ToM QA Dataset, introduced in the EMNLP 2018 paper 'Evaluating Theory of Mind in Question Answering', provides a comprehensive set of scenarios to test question-answering models. The dataset includes first-order and second-order belief questions, as well as memory and reality questions, to ensure models have a correct understanding of the state of the world and others' beliefs. It is available in four versions: easy with noise, easy without noise, hard with noise, and hard without noise.
The CaiTI_dataset repository contains datasets for Motivational Interviewing and Cognitive Behavioral Therapy, curated by therapists to train CaiTI.
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 DAIC-WOZ dataset contains clinical interviews designed to support the diagnosis of psychological distress conditions such as anxiety, depression, and post-traumatic stress disorder. This repository provides code for extracting question-level features from the DAIC-WOZ dataset, which can be used for multimodal analysis of depression levels.