Omkar2118/datasets huggingface dataset:
ChatGPT Software Testing Study
Full name: ChatGPT Software Testing Study Dataset

**ChatGPT Software Testing Study Dataset** contains questions from a well-known software testing book by Ammann and Offutt. It uses all the textbook questions in Chapters 1 to 5 that have solutions available on the book’s official website. These solutions are made publicly available to help students learn. Questions that are not in the student solution are omitted because publishing our results might expose answers that the authors of the book do not intend to make public.

Source: [ChatGPT and Software Testing Education: Promises & Perils](https://arxiv.org/pdf/2302.03287.pdf)

Introducing paper: ChatGPT and Software Testing Education: Promises & Perils
VTQA
Full name: Visual Text Question Answering

VTQA is a dataset containing open-ended questions about image-text pairs. This dataset requires the model to align multimedia representations of the same entity to implement multi-hop reasoning between image and text and finally use natural language to answer the question. The aim of this dataset is to develop and benchmark models that are capable of multimedia entity alignment, multi-step reasoning and open-ended answer generation.
VTQA dataset consists of 10,238 image-text pairs and 27,317 questions. The images are real images from [MSCOCO](https://cocodataset.org/) dataset, containing a variety of entities. The annotators are required to first annotate relevant text according to the image, and then ask questions based on the image-text pair, and finally answer the question open-ended.

Introducing paper: VTQA: Visual Text Question Answering via Entity Alignment and Cross-Media Reasoning