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
E-QGen: Educational Lecture Abstract-based Question Generation System
To optimize the preparation process for educators in academic lectures and associated question-and-answer sessions, this paper presents E-QGen, a lecture abstract-based question generation system.
Consistency Training by Synthetic Question Generation for Conversational Question Answering
In our novel model-agnostic approach, referred to as CoTaH (Consistency-Trained augmented History), we augment the historical information with synthetic questions and subsequently employ consistency training to train a model that utilizes both real and augmented historical data to implicitly make the reasoning robust to irrelevant history.
Which questions should I answer? Salience Prediction of Inquisitive Questions
QSALIENCE is instruction-tuned over our dataset of linguist-annotated salience scores of 1, 766 (context, question) pairs.
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.
SGSH: Stimulate Large Language Models with Skeleton Heuristics for Knowledge Base Question Generation
Knowledge base question generation (KBQG) aims to generate natural language questions from a set of triplet facts extracted from KB.
Explainable Multi-hop Question Generation: An End-to-End Approach without Intermediate Question Labeling
We also prove that our model logically and incrementally increases the complexity of questions, and the generated multi-hop questions are also beneficial for training question answering models.
Improving Socratic Question Generation using Data Augmentation and Preference Optimization
umass-ml4ed/socratic-quest-gen
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The Socratic method is a way of guiding students toward solving a problem independently without directly revealing the solution to the problem.
Qsnail: A Questionnaire Dataset for Sequential Question Generation
Questionnaires entail a series of questions that must conform to intricate constraints involving the questions, options, and overall structure.
TreeEval: Benchmark-Free Evaluation of Large Language Models through Tree Planning
Recently, numerous new benchmarks have been established to evaluate the performance of large language models (LLMs) via either computing a holistic score or employing another LLM as a judge.
AutoTutor meets Large Language Models: A Language Model Tutor with Rich Pedagogy and Guardrails
Large Language Models (LLMs) have found several use cases in education, ranging from automatic question generation to essay evaluation.
