We introduce the CAUS (Curious About Uncertain Scene) 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).

中文翻译：

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CAUS：基于人类认知、利用大型语言模型的问题生成数据集

我们引入了 CAUS（对不确定场景感到好奇）数据集，旨在使大型语言模型（特别是 GPT-4）能够模拟人类认知过程来解决不确定性。利用该数据集，我们研究了法学硕士有效参与提问的潜力。我们的方法包括提供嵌入不确定性的场景描述，以刺激推理和查询的生成。然后根据多维标准对查询进行分类。所有程序均由法学硕士和人类研究人员参与的协作系统促进。我们的结果表明，GPT-4 可以有效地生成相关问题并掌握其细微差别，特别是在给出适当的上下文和说明时。该研究表明，将类人提问融入人工智能模型可以提高其管理不确定性的能力，为人工智能 (AI) 的未来进步铺平道路。