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 multi-dimensional 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).

We introduce CogME, a cognition-inspired, multi-dimensional evaluation metric for AI models focusing on story understanding. CogME is a framework grounded in human thinking strategies and story elements that involve story understanding. With a specific breakdown of the questions, this approach provides a nuanced assessment revealing not only AI models' particular strengths and weaknesses but also the characteristics of the benchmark dataset. Our case study with the DramaQA dataset demonstrates a refined analysis of the model and the benchmark dataset. It is imperative that metrics align closely with human cognitive processes by comprehending the tasks' nature. This approach provides insights beyond traditional overall scores and paves the way for more sophisticated AI development targeting higher cognitive functions.