Document question answering is a task of question answering on given documents such as reports, slides, pamphlets, and websites, and it is a truly demanding task as paper and electronic forms of documents are so common in our society. This is known as a quite challenging task because it requires not only text understanding but also understanding of figures and tables, and hence visual question answering (VQA) methods are often examined in addition to textual approaches. We introduce Japanese Document Question Answering (JDocQA), a large-scale document-based QA dataset, essentially requiring both visual and textual information to answer questions, which comprises 5,504 documents in PDF format and annotated 11,600 question-and-answer instances in Japanese. Each QA instance includes references to the document pages and bounding boxes for the answer clues. We incorporate multiple categories of questions and unanswerable questions from the document for realistic question-answering applications. We empirically evaluate the effectiveness of our dataset with text-based large language models (LLMs) and multimodal models. Incorporating unanswerable questions in finetuning may contribute to harnessing the so-called hallucination generation.

A 360-degree image captures the entire scene without the limitations of a camera’s field of view, which makes it difficult to describe all the contexts in a single caption. We propose a novel task called Query-based Image Captioning (QuIC) for 360-degree images, where a query (words or short phrases) specifies the context to describe. This task is more challenging than the conventional image captioning task, which describes salient objects in images, as it requires fine-grained scene understanding to select the contents consistent with user’s intent based on the query. We construct a dataset for the new task that comprises 3,940 360-degree images and 18,459 pairs of queries and captions annotated manually. Experiments demonstrate that fine-tuning image captioning models further on our dataset can generate more diverse and controllable captions from multiple contexts of 360-degree images.