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 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.
The ISSP is a cross-national collaboration program conducting annual surveys on diverse topics relevant to social sciences. It includes members from various cultures around the globe and provides free access to collected data and documentation.
This paper discusses Helply - a synthesized ML training dataset focused on psychology and therapy, created by Alex Scott and published by NamelessAI. The dataset developed by Alex Scott is a comprehensive collection of synthesized data designed to train LLMs in understanding psychological and therapeutic contexts. This dataset aims to simulate real-world interactions between therapists and patients, enabling ML models to learn from a wide range of scenarios and therapeutic techniques.