Question Generation

The COCO (Common Objects in Context) dataset is a large-scale object detection, segmentation, and captioning dataset. It is designed to encourage research on a wide variety of object categories and is commonly used for benchmarking computer vision models. It is an essential dataset for researchers and developers working on object detection, segmentation, and pose estimation tasks.

The Natural Questions corpus is a question answering dataset containing 307,373 training examples, 7,830 development examples, and 7,842 test examples. Each example is comprised of a google.com query and a corresponding Wikipedia page. Each Wikipedia page has a passage (or long answer) annotated on the page that answers the question and one or more short spans from the annotated passage containing the actual answer. The long and the short answer annotations can however be empty. If they are both empty, then there is no answer on the page at all. If the long answer annotation is non-empty, but the short answer annotation is empty, then the annotated passage answers the question but no explicit short answer could be found. Finally 1% of the documents have a passage annotated with a short answer that is “yes” or “no”, instead of a list of short spans. Source: A BERT Baseline for the Natural Questions Image Source: https://paperswithcode.com/paper/natural-questions-a-benchmark-for-question/

TriviaQA is a realistic text-based question answering dataset which includes 950K question-answer pairs from 662K documents collected from Wikipedia and the web. This dataset is more challenging than standard QA benchmark datasets such as Stanford Question Answering Dataset (SQuAD), as the answers for a question may not be directly obtained by span prediction and the context is very long. TriviaQA dataset consists of both human-verified and machine-generated QA subsets. Source: Episodic Memory Reader: Learning What to Rememberfor Question Answering from Streaming Data Image Source: Joshi et al

Outside Knowledge Visual Question Answering (OK-VQA) includes more than 14,000 questions that require external knowledge to answer. Source: OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge Image Source: https://okvqa.allenai.org/

The SciQ dataset contains 13,679 crowdsourced science exam questions about Physics, Chemistry and Biology, among others. The questions are in multiple-choice format with 4 answer options each. For the majority of the questions, an additional paragraph with supporting evidence for the correct answer is provided. Source: Allen Institute for AI

The Stanford Question Answering Dataset (SQuAD) is a collection of question-answer pairs derived from Wikipedia articles. In SQuAD, the correct answers of questions can be any sequence of tokens in the given text. Because the questions and answers are produced by humans through crowdsourcing, it is more diverse than some other question-answering datasets. SQuAD 1.1 contains 107,785 question-answer pairs on 536 articles. SQuAD2.0 (open-domain SQuAD, SQuAD-Open), the latest version, combines the 100,000 questions in SQuAD1.1 with over 50,000 un-answerable questions written adversarially by crowdworkers in forms that are similar to the answerable ones. Source: Deep Learning Based Text Classification: A Comprehensive Review Image Source: https://rajpurkar.github.io/SQuAD-explorer/explore/v2.0/dev/Prime_number.html

VQG is a collection of datasets for visual question generation. VQG questions were collected by crowdsourcing the task on Amazon Mechanical Turk (AMT). The authors provided details on the prompt and the specific instructions for all the crowdsourcing tasks in this paper in the supplementary material. The prompt was successful at capturing nonliteral questions. Images were taken from the MSCOCO dataset. Source: What BERT Sees: Cross-Modal Transfer for Visual Question Generation

A new large-scale question-answering dataset that requires reasoning on heterogeneous information. Each question is aligned with a Wikipedia table and multiple free-form corpora linked with the entities in the table. The questions are designed to aggregate both tabular information and text information, i.e., lack of either form would render the question unanswerable. Source: HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data

GrailQA is a new large-scale, high-quality dataset for question answering on knowledge bases (KBQA) on Freebase with 64,331 questions annotated with both answers and corresponding logical forms in different syntax (i.e., SPARQL, S-expression, etc.). It can be used to test three levels of generalization in KBQA: i.i.d., compositional, and zero-shot.

FairytaleQA is a dataset focusing on narrative comprehension of kindergarten to eighth-grade students. Annotated by educational experts based on an evidence-based theoretical framework, FairytaleQA consists of 10,580 explicit and implicit questions derived from 278 children-friendly story narratives, covering seven types of narrative elements or relations. It can support narrative Question Generation (QG) and Narrative Question Answering (QA) tasks.

ROPES is a QA dataset which tests a system's ability to apply knowledge from a passage of text to a new situation. A system is presented a background passage containing a causal or qualitative relation(s), a novel situation that uses this background, and questions that require reasoning about effects of the relationships in the back-ground passage in the context of the situation. Source: Allen Institute for AI

The MMD (MultiModal Dialogs) dataset is a dataset for multimodal domain-aware conversations. It consists of over 150K conversation sessions between shoppers and sales agents, annotated by a group of in-house annotators using a semi-automated manually intense iterative process. Source: Towards Building Large Scale Multimodal Domain-Aware Conversation Systems

FreebaseQA is a data set for open-domain QA over the Freebase knowledge graph. The question-answer pairs in this data set are collected from various sources, including the TriviaQA data set and other trivia websites (QuizBalls, QuizZone, KnowQuiz), and are matched against Freebase to generate relevant subject-predicate-object triples that were further verified by human annotators. As all questions in FreebaseQA are composed independently for human contestants in various trivia-like competitions, this data set shows richer linguistic variation and complexity than existing QA data sets, making it a good test-bed for emerging KB-QA systems. Source: FreebaseQA: A New Factoid QA Data Set Matching Trivia-Style Question-Answer Pairs with Freebase

A dataset of ~19K questions that are elicited while a person is reading through a document. Source: Inquisitive Question Generation for High Level Text Comprehension

GLGE is a general language generation evaluation benchmark which is composed of 8 language generation tasks, including Abstractive Text Summarization (CNN/DailyMail, Gigaword, XSUM, MSNews), Answer-aware Question Generation (SQuAD 1.1, MSQG), Conversational Question Answering (CoQA), and Personalizing Dialogue (Personachat).

The question-answer (QA) pairs are automatically generated using state-of-the-art question generation methods based on paintings and comments provided in an existing art understanding dataset. The QA pairs are cleansed by crowdsourcing workers with respect to their grammatical correctness, answerability, and answers' correctness. The dataset inherently consists of visual (painting-based) and knowledge (comment-based) questions. Source: A Dataset and Baselines for Visual Question Answering on Art

ARID is a large-scale, multi-view object dataset collected with an RGB-D camera mounted on a mobile robot. Source: ARID

DiSCQ is a newly curated question dataset composed of 2,000+ questions paired with the snippets of text (triggers) that prompted each question. The questions are generated by medical experts from 100+ MIMIC-III discharge summaries. This dataset is released to facilitate further research into realistic clinical Question Answering (QA) and Question Generation (QG). Source: Learning to Ask Like a Physician Image Source: https://arxiv.org/pdf/2206.02696.pdf

ClarQ, consists of ∼2M examples distributed across 173 domains of stackexchange. This dataset is meant for training and evaluation of Clarification Question Generation Systems. Source: ClarQ: A large-scale and diverse dataset for Clarification Question Generation

TextBox 2.0 is a comprehensive and unified library for text generation, focusing on the use of pre-trained language models (PLMs). The library covers 13 common text generation tasks and their corresponding 83 datasets and further incorporates 45 PLMs covering general, translation, Chinese, dialogue, controllable, distilled, prompting, and lightweight PLMs. Source: TextBox 2.0: A Text Generation Library with Pre-trained Language Models Image Source: https://arxiv.org/pdf/2212.13005v1.pdf

Dataset Description The dataset described in the provided text is focused on social media polls collected from Weibo, a popular Chinese microblogging platform. The dataset aims to empirically study social media polls and analyze user engagement patterns. ### Characteristics of the Dataset - Size: The dataset consists of 20,252 polls collected from 1,860 users on Weibo. - Data Collection: The polls were obtained by sampling Weibo posts containing polls and examining the posting history of the authors. The dataset also includes comments on each post. - Sparsity: The dataset faces the challenge of sparse distribution of polls on Weibo, as less than 0.1% of the randomly gathered posts contained polls. - Content: The dataset includes user-generated polls with questions, answer choices, and corresponding votes. The polls often incorporate trendy hashtags to attract user attention and cover various topics, including social events, public emergencies (such as the COVID-19 outbreak), entertainment topics (celebrities, TV shows), and more. ### Motivations and Summary The motivation behind collecting this dataset is to explore social media polls on Weibo and analyze user engagement patterns. The study aims to understand how users interact with polls, the influence of polls on user engagement, and the types of topics that are more likely to contain polls. The dataset provides insights into user behavior on Weibo by examining factors such as the length of posts, comments, questions, and answers. It also highlights the preference for voting over commenting as a means of expressing opinions. The dataset's analysis suggests that posts with polls tend to attract more comments, likes, and reposts compared to posts without polls. ### Potential Use Cases This dataset can be useful for various research and practical applications, such as: - Social Media Analysis: Researchers can analyze the characteristics and dynamics of social media polls, understanding how they influence user engagement and the types of topics that attract poll creation. - User Engagement Studies: The dataset allows for the exploration of user behavior and preferences when it comes to interacting with polls, providing insights into the factors that drive user engagement on social media platforms. - Trend Analysis: By examining the hashtags associated with polls, the dataset can contribute to understanding social events, public emergencies, and entertainment trends on Weibo. - Marketing and Advertising: The dataset can assist marketers and advertisers in understanding user preferences and interests, enabling them to create targeted campaigns based on the popular topics identified in the dataset. Please note that the actual contents and specific applications of the dataset may vary based on further analysis and research conducted by users.

An enormous question answer pair corpus produced by applying a novel neural network architecture on the knowledge base Freebase to transduce facts into natural language questions. Source: Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus

Dataset for our paper Disambiguation-Centric Finetuning Makes Enterprise Tool-Calling LLMs More Realistic and Less Risky which includes 5000 enterprise tools and the corresponding dialogues generated using DiaFORGE UTC data engine. The dataset is generated with the data generation engine. The engine simulates a user agent and an assistant agent in a dialogue, where the user agent has a persona and the assistant agent has access to a set of tools. Detailed information about the data generation process can be found in the paper. Each entry in the dataset contains: - seed: Gold tool for the generated dialogue. - user_persona: Persona of the simulated user agent. - messages: List of messages in the dialogue, where each message is a dictionary containing: - distractor_tools: List of distractor tools that are not the gold tool but are relevant to the dialogue. These tools are used by user agent to generate utterances that are hard to disambiguate from the gold tool. - retrieved_tools: List of tools retrieved by the assistant agent and used by the assistant agent in order to ask clarifying questions to the user agent.

IDK-MRC is an Indonesian Machine Reading Comprehension (MRC) dataset consists of more than 10K questions in total with over 5K unanswerable questions with diverse question types. Source: IDK-MRC: Unanswerable Questions for Indonesian Machine Reading Comprehension Image Source: https://arxiv.org/pdf/2210.13778v1.pdf

The goal of InfoLossQA is to generate a series of QA pairs that reveal to lay readers what information a simplified text lacks compared to its original. We provide an annotated dataset in the domain of medical text simplification, specifically abstracts of Randomized Controlled Trials (RCTs). The abstracts were automatically simplified by an LLM (GPT-4). Then, three linguists annotated information loss and wrote the QA pairs. For more details about the dataset, see the project website: https://InfoLossQA.ikim.nrw/#/