This work introduces a novel task, location-aware visual question generation
(LocaVQG), which aims to generate engaging questions from data relevant to a
particular geographical location. Specifically, we represent such
location-aware information with surrounding images and a GPS coordinate. To
tackle this task, we present a dataset generation pipeline that leverages GPT-4
to produce diverse and sophisticated questions. Then, we aim to learn a
lightweight model that can address the LocaVQG task and fit on an edge device,
such as a mobile phone. To this end, we propose a method which can reliably
generate engaging questions from location-aware information. Our proposed
method outperforms baselines regarding human evaluation (e.g., engagement,
grounding, coherence) and automatic evaluation metrics (e.g., BERTScore,
ROUGE-2). Moreover, we conduct extensive ablation studies to justify our
proposed techniques for both generating the dataset and solving the task.

本研究介绍了一种新颖的任务，即基于位置感知的视觉问题生成（LocaVQG），旨在从与特定地理位置相关的数据中生成引人入胜的问题。通过周围图像和 GPS 坐标将这种位置感知信息进行表示，并提出了一种利用 GPT-4 生成多样化和高级问题的数据集生成流程。然后，我们旨在学习一种轻量级模型，能够解决 LocaVQG 任务并适用于边缘设备，例如手机。为此，我们提出了一种可靠地从位置感知信息生成引人入胜问题的方法。我们所提出的方法在人工评估（如参与度，基准，连贯性）和自动评估指标（如 BERTScore，ROUGE-2）方面优于基线。此外，我们还进行了大量割离研究，以证明我们提出的数据集生成技术和任务解决方法的合理性。