Question Generation
222 papers with code • 10 benchmarks • 25 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
XQuAD
SciQ
VQG
HybridQA
ROPES
Latest papers with no code
RAGAR, Your Falsehood RADAR: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Models
The escalating challenge of misinformation, particularly in the context of political discourse, necessitates advanced solutions for fact-checking.
CAUS: A Dataset for Question Generation based on Human Cognition Leveraging Large Language Models
We introduce the CAUS (Curious About Uncertain Scene) dataset, designed to enable Large Language Models, specifically GPT-4, to emulate human cognitive processes for resolving uncertainties.
Understanding the Role of Temperature in Diverse Question Generation by GPT-4
We conduct a preliminary study of the effect of GPT's temperature parameter on the diversity of GPT4-generated questions.
Question Generation in Knowledge-Driven Dialog: Explainability and Evaluation
We explore question generation in the context of knowledge-grounded dialogs focusing on explainability and evaluation.
Generating Uncontextualized and Contextualized Questions for Document-Level Event Argument Extraction
This paper presents multiple question generation strategies for document-level event argument extraction.
On Few-Shot Prompting for Controllable Question-Answer Generation in Narrative Comprehension
A controllable question generation scheme focuses on generating questions with specific attributes, allowing better control.
Reference-based Metrics Disprove Themselves in Question Generation
These criteria are not constrained to the syntactic or semantic of a single reference question, and the metric does not require a diverse set of references.
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.
