Question Generation
223 papers with code • 8 benchmarks • 23 datasets
The goal of Question Generation is to generate a valid and fluent question according to a given passage and the target answer. Question Generation can be used in many scenarios, such as automatic tutoring systems, improving the performance of Question Answering models and enabling chatbots to lead a conversation.
Benchmarks
These leaderboards are used to track progress in Question Generation
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Libraries
Use these libraries to find Question Generation models and implementations
MS COCO
SQuAD
Natural Questions
TriviaQA
OK-VQA
SciQ
VQG
HybridQA
ROPES
FairytaleQA
Latest papers with no code
Choose Your Own Adventure: Interactive E-Books to Improve Word Knowledge and Comprehension Skills
Students read two e-Books that taught word learning and comprehension monitoring strategies in the service of learning difficult vocabulary and targeted science concepts about hurricanes.
VBART: The Turkish LLM
Our work shows that having a pre-trained LLM for Turkish outperforms up to 3x multilingual models, improving existing results and providing efficient models for training and inference.
A Survey on Neural Question Generation: Methods, Applications, and Prospects
In this survey, we present a detailed examination of the advancements in Neural Question Generation (NQG), a field leveraging neural network techniques to generate relevant questions from diverse inputs like knowledge bases, texts, and images.
NewsQs: Multi-Source Question Generation for the Inquiring Mind
We present NewsQs (news-cues), a dataset that provides question-answer pairs for multiple news documents.
ConVQG: Contrastive Visual Question Generation with Multimodal Guidance
However, generating focused questions using textual constraints while enforcing a high relevance to the image content remains a challenge, as VQG systems often ignore one or both forms of grounding.
Identifying Factual Inconsistency in Summaries: Towards Effective Utilization of Large Language Model
Factual inconsistency poses a significant hurdle for the commercial deployment of abstractive summarizers.
Extending Interactive Science Exhibits into the Classroom using Anthropomorphized Chatbots and Bloom's Taxonomy
We hypothesize that turning exhibits into first-person anthropomorphized chatbots with a personality, like quirky-talking asteroids or comets, can increase engagement and learning.
How the Advent of Ubiquitous Large Language Models both Stymie and Turbocharge Dynamic Adversarial Question Generation
Dynamic adversarial question generation, where humans write examples to stump a model, aims to create examples that are realistic and informative.
Q&A Prompts: Discovering Rich Visual Clues through Mining Question-Answer Prompts for VQA requiring Diverse World Knowledge
With the breakthrough of multi-modal large language models, answering complex visual questions that demand advanced reasoning abilities and world knowledge has become a much more important testbed for developing AI models than ever.
Advancing Large Multi-modal Models with Explicit Chain-of-Reasoning and Visual Question Generation
The increasing demand for intelligent systems capable of interpreting and reasoning about visual content requires the development of Large Multi-Modal Models (LMMs) that are not only accurate but also have explicit reasoning capabilities.
