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Question Generation

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.

Source: Generating Highly Relevant Questions

Papers

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Showing 101–150 of 664 papers

TitleStatusHype
Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEsCode1
Knowledge Graph Simple Question Answering for Unseen DomainsCode1
C3VQG: Category Consistent Cyclic Visual Question GenerationCode1
Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking ConversationsCode1
Semantic Graphs for Generating Deep QuestionsCode1
Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question AnsweringCode1
PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned GenerationCode1
Toward Subgraph-Guided Knowledge Graph Question Generation with Graph Neural NetworksCode1
UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-TrainingCode1
Asking Questions the Human Way: Scalable Question-Answer Generation from Text CorpusCode1
ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language GenerationCode1
R2DE: a NLP approach to estimating IRT parameters of newly generated questionsCode1
ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-trainingCode1
Visual Dialogue State Tracking for Question GenerationCode1
Cross-Lingual Natural Language Generation via Pre-TrainingCode1
Interconnected Question Generation with Coreference Alignment and Conversation Flow ModelingCode1
Large-Scale Answerer in Questioner's Mind for Visual Dialog Question GenerationCode1
Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention NetworksCode1
Improving Neural Question Generation using Answer SeparationCode1
Plan, Attend, Generate: Planning for Sequence-to-Sequence ModelsCode1
Learning to Ask: Neural Question Generation for Reading ComprehensionCode1
Neural Question Generation from Text: A Preliminary StudyCode1
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence ModelsCode1
Compressed and Smooth Latent Space for Text Diffusion Modeling—0
ELLIS Alicante at CQs-Gen 2025: Winning the critical thinking questions shared task: LLM-based question generation and selection—0
Knowledge Compression via Question Generation: Enhancing Multihop Document Retrieval without Fine-tuning—0
Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights—0
TO-GATE: Clarifying Questions and Summarizing Responses with Trajectory Optimization for Eliciting Human Preference—0
Ask, Retrieve, Summarize: A Modular Pipeline for Scientific Literature SummarizationCode0
Multi-Hop Question Generation via Dual-Perspective Keyword GuidanceCode0
AutoGEEval: A Multimodal and Automated Framework for Geospatial Code Generation on GEE with Large Language Models—0
KG-QAGen: A Knowledge-Graph-Based Framework for Systematic Question Generation and Long-Context LLM EvaluationCode0
Leveraging Graph Retrieval-Augmented Generation to Support Learners' Understanding of Knowledge Concepts in MOOCs—0
Self Rewarding Self Improving—0
KAQG: A Knowledge-Graph-Enhanced RAG for Difficulty-Controlled Question Generation—0
Enhancing the Learning Experience: Using Vision-Language Models to Generate Questions for Educational VideosCode0
Harnessing Structured Knowledge: A Concept Map-Based Approach for High-Quality Multiple Choice Question Generation with Effective DistractorsCode0
RV-Syn: Rational and Verifiable Mathematical Reasoning Data Synthesis based on Structured Function Library—0
Context Selection and Rewriting for Video-based Educational Question GenerationCode0
AskQE: Question Answering as Automatic Evaluation for Machine Translation—0
Evaluating the Fitness of Ontologies for the Task of Question Generation—0
TransientTables: Evaluating LLMs' Reasoning on Temporally Evolving Semi-structured Tables—0
Follow-up Question Generation For Enhanced Patient-Provider Conversations—0
ECLAIR: Enhanced Clarification for Interactive Responses—0
ECLAIR: Enhanced Clarification for Interactive Responses in an Enterprise AI Assistant—0
Cross-Examiner: Evaluating Consistency of Large Language Model-Generated Explanations—0
QG-SMS: Enhancing Test Item Analysis via Student Modeling and Simulation—0
What is a Good Question? Utility Estimation with LLM-based Simulations—0
Savaal: Scalable Concept-Driven Question Generation to Enhance Human Learning—0
QuOTE: Question-Oriented Text Embeddings—0
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All datasetsSQuAD1.1COCO Visual Question Answering (VQA) real images 1.0 open endedGrailQA-CompositionalGrailQA-IIDGrailQA-Zero-ShotFairytaleQAWeiboPollsNatural QuestionsSQuADTriviaQAVisual Question Generation

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ERNIE-GENLARGE (beam size=5)BLEU-425.41—Unverified
2BART (TextBox 2.0)BLEU-425.08—Unverified
3ProphetNet + ASGenBLEU-424.44—Unverified
4UniLMv2BLEU-424.43—Unverified
5ProphetNet + syn. mask + localnessBLEU-424.37—Unverified
6ProphetNetBLEU-423.91—Unverified
7UniLM + ASGenBLEU-423.7—Unverified
8UniLMBLEU-422.78—Unverified
9BERTSQGBLEU-422.17—Unverified
10Selector & NQG++BLEU-415.87—Unverified
#ModelMetricClaimedVerifiedStatus
1MDNBLEU-165.1—Unverified
2coco-Caption [[Karpathy and Li2014]]BLEU-162.5—Unverified
3Max(Yang,2015)BLEU-159.4—Unverified
4Sample(Yang,2015)BLEU-138.8—Unverified
#ModelMetricClaimedVerifiedStatus
1FactJointGTMETEOR36.21—Unverified
2JointGTMETEOR36.08—Unverified
3FactT5BMETEOR35.72—Unverified
4T5BMETEOR35.64—Unverified
#ModelMetricClaimedVerifiedStatus
1FactT5BBLEU46.1—Unverified
2JointGTBLEU45.95—Unverified
3T5BBLEU44.51—Unverified
4FactJointGTBLEU43.61—Unverified
#ModelMetricClaimedVerifiedStatus
1JointGTMETEOR37.69—Unverified
2FactJointGTMETEOR37.55—Unverified
3FactT5BMETEOR37.39—Unverified
4T5BMETEOR37.35—Unverified
#ModelMetricClaimedVerifiedStatus
1BART fine-tuned on FairytaleQAROUGE-L0.53—Unverified
2BART fine-tuned on NarrativeQA and FairytaleQAROUGE-L0.52—Unverified
3BART fine-tuned on NarrativeQAROUGE-L0.44—Unverified
#ModelMetricClaimedVerifiedStatus
1UniPollROUGE-149.6—Unverified
2T5ROUGE-144.46—Unverified
3Dual DecROUGE-138.24—Unverified
#ModelMetricClaimedVerifiedStatus
1Info-HCVAEQAE37.18—Unverified
2HCVAEQAE31.45—Unverified
#ModelMetricClaimedVerifiedStatus
1Info-HCVAEQAE71.18—Unverified
2HCVAEQAE69.46—Unverified
#ModelMetricClaimedVerifiedStatus
1Info-HCVAEQAE35.45—Unverified
2HCVAEQAE30.2—Unverified
#ModelMetricClaimedVerifiedStatus
1MDNBLEU-136—Unverified