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 iBVP dataset is a collection of synchronized RGB and thermal infrared videos with PPG ground-truth signals acquired from an ear. It includes manual signal quality labels and dense signal-quality assessment using the SQA-PhysMD model. The dataset is designed to induce real-world variations in psycho-physiological states and head movement.
Dataset Card for Psychology Therapy Dataset : This dataset card aims to provide information about a dataset focused on psychology therapy conversations. Language(s) (NLP): Turkish (tr)
MentalManip数据集是由Wang等人(2024b)引入的,专门用于检测和分类心理操纵的对话数据集。该数据集包含4000个多轮虚构对话,来源于在线电影剧本,并进行了多层次的标注,包括操纵的存在、操纵技巧和目标脆弱性。数据集的创建旨在通过高质量的标注确保数据的一致性和准确性,从而支持心理操纵检测的研究。