ToM QA Dataset: Evaluating Theory of Mind in Question Answering
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.
Αναλυτική Εισαγωγή
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.
ΠερισσότεραΣύνολο δεδομένων
Adolescent Mental Health: WHO Report
The WHO report on adolescent mental health describes actions undertaken by international development organizations to address adolescents’ mental health needs at the country level. It highlights the inadequacy of current efforts and the need for more coordinated and comprehensive interventions.
APA PsycInfo: Behavioral and Social Sciences Database
APA PsycInfo is the premier abstracting and indexing database covering the behavioral and social sciences. It provides over 5,000,000 peer-reviewed records, 144 million cited references, and spans 600 years of content. The database is updated twice-weekly and includes research in 30 languages from 50 countries.
t4d: Conversion Algorithm from ToMi to T4D Dataset
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.