CAUS: A Dataset for Question Generation based on Human Cognition Leveraging Large Language Models

作     者：Shin, Minjung Kim, Donghyun Ryu, Jeh-Kwang 

作者机构：Interdisciplinary Program in Cognitive Science Seoul National University Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Department of Physical Education Dongguk University Pildong-ro Jung-gu Seoul08826 Korea Republic of 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Large datasets 

摘      要：We introduce the Curious About Uncertain Scene (CAUS) dataset, designed to enable Large Language Models, specifically GPT-4, to emulate human cognitive processes for resolving uncertainties. Leveraging this dataset, we investigate the potential of LLMs to engage in questioning effectively. Our approach involves providing scene descriptions embedded with uncertainties to stimulate the generation of reasoning and queries. The queries are then classified according to multidimensional criteria. All procedures are facilitated by a collaborative system involving both LLMs and human researchers. Our results demonstrate that GPT-4 can effectively generate pertinent questions and grasp their nuances, particularly when given appropriate context and instructions. The study suggests that incorporating human-like questioning into AI models improves their ability to manage uncertainties, paving the way for future advancements in Artificial Intelligence (AI). Copyright © 2024, The Authors. All rights reserved.