Abstract
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).
Abstract (translated)
我们提出了CAUS（好奇关于不确定场景）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。利用这个数据集，我们研究了LLMs有效地参与提问的潜力。我们的方法包括向场景中嵌入带有不确定性的描述，以刺激推理和查询的生成。然后对查询进行多维度的分类。所有程序都由LLMs和人类研究人员合作的系统来促进。我们的结果表明，GPT-4可以有效地生成相关问题并把握其细微差别，尤其是在给出适当的背景和指令时。这项研究建议，将人类类似的问题引入AI模型可以提高它们处理不确定性的能力，为未来人工智能（AI）的进一步发展铺平道路。
URL
https://arxiv.org/abs/2404.11835
